Natural history, synthesis papers and the academic caste system

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It’s been argued that in ecology, like politics, everything is local.

You can’t really understand ecological relationships in nature, unless you’re familiar with the organisms in their natural environment. Or maybe not. That’s probably not a constructive argument. My disposition is that good ecological questions are generated from being familiar with the life that organisms out of doors. But that’s not the only way to do ecology. Continue reading

Undergraduate research is many things

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Conversations about “undergraduate research” often involve dispelling misconceptions.

Undergraduate research is not one thing.

What is undergraduate research? It is research that involves undergraduates. That’s all, nothing else. If you want it to mean something else, you might have to spell it out.

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Continue reading

How to promote inclusivity in graduate fellowships?

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Students who did their undergraduate work at elite universities are dominating access to federally funded graduate fellowships in the sciences. I pointed out this obvious fact at the beginning of this month, which to my surprise caught quite a bit of attention. I also got a lot of email (which I discuss here — it’s more interesting than you might expect).

A common response was: Okay, that’s the problem, what about solutions? Hence, this post. First, here are some facts that are are germane to the solutions. Continue reading

Elite vs. disadvantaged institutions, and NSF Graduate Fellowships: a peek inside the mailbag

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I’ll be soon be sharing specific ideas about what can be done about the disadvantages experienced by talented students who attend non-prestigious undergraduate institutions. But first, I thought it would be useful for me to share how this topic has affected my inbox.

I barely get any email related to this site. Aside from the site stats, and some interactions on twitter, I wouldn’t have any other indicator about readership. So when I receive the occasional email related to this site, it stands out.

In relative terms, I got several metric tons of emails about last week’s post about NSF graduate fellowships. Continue reading

Academic Moneyball

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Over the past week, I’ve been reading Moneyball, by Michael Lewis.moneyball cover

I’m not a baseball person (though I do keep tabs on football soccer). I found Moneyball to be interesting in its own right, but particularly when considering how its lessons may be applied to academic culture.

Lewis tells the story of Billy Beane, the manager of a small-budget major league baseball team, who assembled a crew that was better than most big-budget competitors. How did Beane pull this off?

According to Moneyball, Beane saw through the intellectually inbred and reality-challenged worldview that permeated the baseball community at the time. Scouts were picking players — and offering them humongous salaries — on the basis of athletic traits that didn’t help teams win games. Continue reading

Is it harder, or easier, to publish in your field?

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It takes time and effort to publish a paper. After all, if it were really easy, then publications wouldn’t be a workable (albeit flawed) currency in for success in the sciences.

I often have heard about how some labs experience a bigger or smaller MPU (minimum publishable unit) than others, as I’ve worked in biology departments with a lot of academic diversity.

For example, I once knew an immunologist in an undergraduate institution who spent five years of consistently applied effort, to generate a single paper on a smallish-scale project. This wasn’t a problem in the department, as everyone accepted the notion that the amount of work that it took to generate a paper on this topic was greater than what it would take for (say) physiology, vertebrate paleontology, or ecology. Continue reading

Authorship when the first author is the senior author

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Authorship conventions are based around assumptions that research was done under the umbrella of a research institution.

It’s often just fine to assume that the first author did the most work, and the last author is the senior author who is the PI of the lab that enabled the project.

That’s a fair assumption, so long as the senior author and the first author are different people. In my circumstance, when a paper comes out of my lab, I’m typically the first author and the senior author. Continue reading

Does your campus allow Federal Work Study awards for undergraduate research?

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I used to have Work-Study students doing research in my lab, when I was visiting faculty at Gettysburg College. Then I got a job somewhere else, and I couldn’t do that anymore.

The university where I now work does not assign Work-Study students to work with professors, just like my previous employer. There was a clear institutional policy that prohibited using Federal Work-Study awards to fill undergraduate research positions. Continue reading

Which institutions request external review for tenure files?

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Today, I’m submitting my file for promotion. It’s crazy to think I submitted my most recent tenure file five years ago, it feels closer to yesterday. Unless I get surprised (and it wouldn’t be the first time), I’ll be a full Professor if I’m here next year. And yet, throughout this entire process, there has been zero external validation of tenure and promotion. I think this is really odd. Continue reading

Chairing a search committee, in hindsight

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Last year, I had the dubious honor of chairing a search committee for two positions in my department. The speciality was open. I learned about my department and my university by seeing it through the eyes of applicants and would-be applicants. There’s a lot I’d like to say about the process that I can’t, or shouldn’t, say. But I do have some observations to share. Continue reading

One person’s story about post-PhD employment

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I’m an Associate Professor at a regional state university. How did I get here? What choices did I make that led me in this direction? This month, a bunch of folks are telling their post-PhD stories, led by Jacquelyn Gill. (This group effort constitutes a “blog carnival.”) Here’s my contribution.

I went to grad school because I loved to do research in ecology, evolution and behavior. I knew when I started that I’d be better off having been (meagerly) employed for five years to get a PhD.

The default career mode, at least at the time, was that grad students get a postdoc and then become a professor. It was understood that not everybody would want to, or be able to, follow this path. But is still the starting place in any discussion of post-PhD employment. As time progressed in grad school, I came to the conclusion that I didn’t want to run a lab at a research university, and that I wanted an academic position that combined research, teaching and some outreach.

I liked the idea of working at an R1 institution, but there were three dealbreakers. First, I didn’t want the grant pressure to keep my people employed and to maintain my own security of employment. Second, I wanted to keep it real and run a small lab so that I could be involved in all parts of the science. I didn’t want to be like all of the other PIs that only spent a few days in the field and otherwise were computer jockeys managing people and paper. Third, I was taught in grad school that the life of an R1 PI is less family-friendly than a faculty position at a non-R1 institution. In hindsight, now that I have worked at a few non-R1 institutions, I can tell you that these reasons are total bunk. I was naïve. My reasons for avoiding R1 institutions were not valid and not rooted in reality. Even though I now realize my reasons at the time were screwed up, I was primarily looking for faculty jobs at liberal arts colleges and other teaching-centered institutions.

We muddled through a two-body problem. My spouse wasn’t an academic, but needed a large city to work. She was early enough in her career that she was prepared to move for me while I did the postdoc job hop. I wouldn’t have wanted her to uproot from a good situation. In hindsight, our moves ended up being beneficial for both of us.

As I was approaching the finish to grad school, I was getting nervous about a job. My five years of guaranteed TA support were ending. I recall being very anxious. I landed a postdoc, though the only drawback was starting four months before defending my thesis. I moved from Colorado to Texas for my postdoc, and spent the day on the postdoc and the evenings finishing up my dissertation. As a museum educator, my spouse quickly found a job in the education department at the Houston Museum of Nature and Science.

While I was applying for postdocs, I also applied for faculty positions, even though I was still ABD. And surprisingly enough, I got a couple interviews. (I think I had 2-3 pubs at the time, one of which was in a fancy journal.) I got offered a 2-year sabbatical replacement faculty position at Gettysburg College, an excellent SLAC in south central Pennsylvania. At the same time, my spouse was deciding to go to grad school for more advanced training in museum education. By far, the best choice for her was to study at The George Washington University (don’t forget the ‘The’) in Washington, D.C. This seemed like a relatively magical convergence. With uncertainty for long-term funding in my postdoc (and also no shortage of problems with the project itself), we bailed on Texas and headed back east.

We lived in Frederick, Maryland. Which at the time was the only real city between Washington DC and Gettysburg. (Since then, I’ve heard it’s been converted into an exurb of DC.) I drove past the gorgeous Catoctin mountains every day to go to work, and she took car/metro into DC to work and started grad school. We scheduled her grad school so that she’d finish up when my two-year stint at Gettysburg would be over. I taught a full courseload for the first time, and noticed that I really liked the teaching/research gig at a small college. Grad school was great for my spouse. Life was good. In my first year as a Visiting Assistant Professor, I got four tenure-track job interviews.

Through a magical stroke of fortune, I got a tenure-track job offer in my wife’s hometown, in San Diego, just 2 to 5 hours away from my family in LA (depending on traffic). The only catch was that I’d have to leave my position at Gettyburg one year early, and my wife had one year left in grad school. But, I really needed to focus on starting out my tenure-track position, and she really had to focus on grad school. She could move to DC instead of splitting the commute with me, and I could figure out San Diego without her for a year. If kids were involved, this scenario would have been a lot more complicated. If my spouse’s career was at a more advanced stage, the move from grad school to postdoc to temporary faculty to tenure-track faculty would have a lot messier and would have required more compromises. But somehow we made it work and it felt something resembling normal.

Then, after working in San Diego for seven years, we moved up to Los Angeles. I already have told that story. Which, if you haven’t read it, is a nail-biter.

As I tell the story to non-academics, they find our peregrinations rather surprising. From LA, to Boulder, to Houston, to Maryland, to San Diego, and eventually back to LA, at least for the last seven years. (In the meanwhile, I’ve been going back and forth from my field site Costa Rica on a regular basis). This frequency of moving is entirely normal in academia, even if we look like vagabonds among our friends.

What do I offer as the take-home interpretations of my post-PhD job route?

First: The geography of my tenure-track job offers was lucky. To some extent, I’ve made this luck through persistence, but having landed a job in my wife’s hometown was pretty damn incredible. And after botching the first one entirely, getting one in my hometown was amazing. Now that my spouse is at the senior staff level, openings in her specialized field of museum education are about as rare and prized as in my own field. However, we now live in a big city with many universities and many world-class museums, so we can (theoretically) move jobs without moving our home. We now are juggling a three-body problem.

Second: My early choices constrained later options. Even though I no longer am wary of an R1 faculty position, after spending several years at teaching-focused universities that is a long shot for me. (I do several people who made that move, but it’s still a rarity.) I’m confident that I can operate a helluva research program at a highly-ranked R1, but I’m too senior for an entry-level tenure-track position, and not a rockstar who will be recruited for a senior-level hire. For example, I am confident that I would totally kick butt at UCLA just up the road, but I doubt a search committee there will reach the same conclusion. I am just as pleased to be at a non-prestigious regional university, and when I do move, it’ll be because I’ll be looking for better compensation and working conditions. I’m looking at working at all kinds of universities, and I think my job satisfaction will be more tied to local factors on an individual campus rather than the type of institution.

Third: I applied for jobs that many PhD students and postdocs think are unsuitable for themselves. I spent a lot of time creating applications for universities that I’ve never heard of. I was hired as an “ecosystem ecologist” at CSU Dominguez Hills in Los Angeles. Even though I grew up in Los Angeles, the first time I ever heard of CSU Dominguez Hills is when I saw the job ad. And I’m not an ecosystem ecologist either. That didn’t keep me from spending several hours tailoring my application for this particular job. But I wouldn’t have gotten this job unless I applied, and most postdocs are not applying for jobs like the one I have now. I know this from chairing a search committee for two positions last year. That’s a whole ‘nother story.

Fourth: Is being a professor my most favorite job ever? Actually, no. My employment paradise would be a natural history museum, with a mix of research, outreach and occasional teaching. I’m not a systematist or an evolutionary biologist, so getting hired into this kind of job is not likely. However, I have had a couple interviews for curatorial-esque positions over the last ten years and was exceptionally bummed that I didn’t get them. On the balance, even large museums go through phases of financial instability. It would be hard to give up tenure for a job that might bounce me to the street because of the financial misdeeds of board members and museum leadership. I’ve seen too many talented good museum people lose positions due to cutbacks or toxic administrators. I don’t know what could get me to take off the golden handcuffs of tenure. There are some university museums that hire faculty. That would be wonderful. Maybe someday that could happen. But I am pleased with what I’m doing, and I still am amazed that there are people paying me to do what I love.

Fifth: I ruled out a number of possibilities for family reasons. There are a variety of locations where I would be able to find work but would be unworkable for my spouse. Even in the depth of a job crisis, I opted against a number of options that would’ve given me strong and steady employment.

Sixth: I am not employed as a professor because I deserve it more than others. There are others equally, and more, deserving that are underemployed compared to my position in the academic caste system. The CV I had when I got my first academic position probably wouldn’t be able to do so now, 15 years later.

How much do you let students design projects?

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Now is the time of year when we work with students on designing summer research projects. How do you decide exactly what their project is, and how the experimental design is structured? This is something I struggle with.

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Image by T.McGlynn

In theory, quality mentorship (involving time, patience and skill) can lead a student towards working very independently and still have a successful project. Oftentimes, though, the time constraints involved in a summer project don’t allow for a comprehensive mentoring scheme that facilitates a high level of student independence. Should the goal of a student research project be training of an independently-thinking scientist or the production of publishable research? I think you can have both, but when push comes to shove, which way do you have to lean? I’ve written about this already. (Shorter: without the pubs, my lab would run out of dough and then no students would have any experiences. As is said, your mileage may vary.)

A well-designed project will require a familiarity with prior literature, experimental design, relevant statistical approaches and the ability to anticipate the objections that reviewers will have once the final product goes out for review. Undergraduates are typically lacking in most, if not all, of these traits. Sometimes you just gotta tell the student what will work and what will not, and what is important to the scientific community and what is not. And sometimes you can’t send the student home to read fifteen papers before reconsidering a certain technique or hypothesis.

When students in the lab are particularly excited about a project beyond my mentorable expertise, or beyond the realm of publishability, I don’t hesitate to advise a new course. I let them know what I hope students get out a summer research experience:

  • a diverse social network of biologists from many subfields and universities

  • experience designing and running an experiment

  • a pub

All three of those things take different kinds of effort, but all three are within reach, and I make decisions with an effort to maximize these three things for the students. Which means that, what happens in my lab inhabits the right side of the continuum, sometimes on the edge of the ‘zone of no mentorship’ if I take on too many students.

You might notice one thing is missing from my list: conceive an experiment and develop the hypotheses being tested.

Students can do that in grad school if they want. Or in the lab of a different PI. I would rather have a students design experiments on hypotheses connected to my lab that I am confident can be converted into papers, rather than work on an experiments of the students’ own personal interest. (Most of my students become enamored of their experimental subnets pretty quickly, though.)

This approach is in the interest of myself to maintain a productive lab, but I also think that being handed a menu of hypotheses instead of a blank slate is also in the long-term interest of most students. I’m not keen on mentoring a gaggle of students who design their own projects when these projects are only for their edification, and not for sharing with the scientific community. That kind of thing is wonderful for the curriculum, but not for my research lab.

Other people have other approaches, and that is a Good Thing. We need many kinds of PIs, including those that give students so much latitude that they will have an opportunity to learn from failure. And also those that take on 1-2 students at a time and work with them very carefully. I like the idea of thinking about my approach to avoid falling into a default mode of mentorship. Does this scheme make sense, and if it does, where do you fit in and how have you made your choices? I would imagine the nature of your institution and the nature of your subfield — and how much funding is available — structures these choices.

What kind of faculty job do you want?

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Faculty jobs involve teaching, research, and mentoring. Different kinds of universities expect faculty to conduct these activities in different proportions. What is your ideal balance? Consider the figure to find out where you belong.

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Figure by T McGlynn

For the uninitiated, SLAC indicates “Small Liberal Arts College.”

This figure implies a lot of mechanisms that differentiate institutions, and there are a bunch of reasons why the distribution for a regional comprehensive (where I work currently) fills in the gaps that other institutions don’t occupy.

Why I prefer anonymous peer reviews

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Nowadays, I rarely sign my reviews.

In general, I think it’s best if reviews are anonymous.  This is my opinion as an author, as a reviewer, and as an editor. What are my reasons? Anonymous reviews might promote better science, facilitate a more even paying field, and protect junior scientists.

The freedom to sign reviews without negative repercussions is a manifestation of privilege. The use of signed reviews promotes an environment in which some have more latitude than others. When a tenured professor such as myself signs reviews, especially those with negative recommendations, I’m exercising liberties that are not as available to a PhD candidate.

To explain this, here I describe and compare the potential negative repercussions of signed and unsigned reviews.

Unsigned reviews create the potential for harm to authors, though this harm may be evenly distributed among researchers. Arguably, unsigned reviews allow reviewers to be sloppy and get away with a less-than-complete evaluation, which will cause the reviewer to fall out of the good graces of the editor, but not that of the authors. Also, reviewer anonymity allows scientific competitors or enemies to write reviews that unfairly trash (or more strategically sabotage) the work of one another. Junior scientists may not have as much social capital to garner favorable reviews from friends in the business as senior researchers. But on the other hand, anonymous reviews can mask the favoritism that may happen during the review process, conferring an advantage to senior researchers with a larger professional network.

Signed reviews create the potential for harm to reviewers, and confer an advantage to influential authors. It would take a brave, and perhaps foolhardy, junior scientist to write a thorough review of a poor-quality paper coming from the lab of an established senior scientist. This could harm the odds of landing a postdoc, getting a grant funded, or getting a favorable external tenure evaluation. Meanwhile, senior scientists may have more latitude to be critical without fear of direct effects on the ability to bring home a monthly paycheck. Signed reviews might allow more influential scientists to experience a breezier peer review experience than unknown authors.

When the identity of reviewers is disclosed, these data may result in more novel game theoretical strategies that may further subvert the peer-review process. For example, I know there are some reviewers out there who seem to really love the stuff that I do, and there is at least one (and maybe more) who appear to have it in for me. It would only be rational for me to list the people who give me negative reviews as non-preferred reviewers, and those who gave positive reviews as recommended reviewers. If I knew who they were. If everybody knew who gave them more positive and more negative reviews, some people would make choices to help them exploit the system to garner more lightweight peer review. The removal of anonymity can open the door to corruption, including tit-for-tat review strategies. Such a dynamic in the system would further exacerbate the asymmetries between the less experienced and more experienced scientists.

The use of signed reviews won’t stop people from sabotaging other papers. However signed reviews might allow more senior researchers to use their experience with the review system to exploit it in their favor. It takes experience receiving reviews, writing reviews, and handling manuscripts to anticipate the how editors respond to reviews. Of course, let’s not undersell editors, most of whom I would guess are savvy people capable of putting reviews in social context.

I’ve heard a number people say that signing their reviews forces them to write better reviews. This implies that some may use the veil of their identity to act less than honorably or at least not try as hard. (If you were to ask pseudonymous science bloggers, most would disagree.) While the content of the review might be substantially the same regardless of identity, a signed review might be polished with more varnish. I work hard to be polite and write a fair review regardless of whether I put my name on it. But I do admit that when I sign a review, I give it a triple-read to minimize the risk that something could be taken the wrong way (just as whenever I publish a post on this site). I wouldn’t intentionally say anything different when I sign, but it’s normal to take negative reviews personally, so I try to phrase things so that the negative feelings aren’t transferred to me as a person.

I haven’t always felt this way. About ten years ago, I consciously chose to sign all of my reviews, and I did this for a few years.  I observed two side effects of this choice. The first one was a couple instances of awkward interactions at conferences. The second was an uptick in the rate which I was asked to review stuff. I think this is not merely a correlative relationship, because a bunch of the editors who were hitting me up for reviews were authors of papers that I had recently reviewed non-anonymously. (This was affirmation that I did a good job with my reviews, which was nice. But as we say, being a good reviewer and three bucks will get you a cup of coffee.)

Why did I give up signing reviews? Rejection rates for journals are high; most papers are rejected. Even though my reviews, on average, had similar recommendations as other reviewers, it was my name as reviewer that was connected to the rejection. My subfields are small, and if there’s someone who I’ve yet to meet, I don’t want my first introduction to be a review that results in a rejection.

Having a signed review is different than being the rejecting subject editor. As subject editor, I point to reviews to validate the decision, and I also have my well-reasoned editor-in-chief, who to his credit doesn’t follow subject editor recommendations in a pro forma fashion. The reviewer is the bad guy, not the editor. I don’t want to be identified as the bad guy unless it’s necessary. Even if my review is affirming, polite, and as professional as possible in a good way, if the paper is rejected, I’m the mechanism by which it’s rejected. My position at a teaching-focused institution places me on the margins of the research community, even if I am an active researcher. Why the heck would I put my name on something that, if taken the wrong way, could result in further marginalization?

When do I sign? There are two kinds of situations. First, some journals ask us to sign, and I will for high-acceptance rate journals. Second, if I recommend changes involving citations to my own work, I sign. I don’t think I’ve ever said “cite my stuff” when uncited, but sometimes a paper that cites me and follows up on something in my own work, and I step in to clarify. It would be disingenuous to hide my identity at that point.

The take home message on peer review is: The veil of anonymity in peer review unfairly confers advantages to influential researchers, but the removal of that veil creates a new set of more pernicious effects for less influential researchers.

Thanks to Dezene Huber whose remark prompted me to elevate this post from the queue of unwritten posts.

Negotiating for a faculty position: An anecdote, and what to do

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This post is about a revoked job offer at a teaching institution that was in the news, and is also about how to negotiate for a job. I’ve written about negotiation priorities before, but this missive is about how to discuss those priorities with your negotiating partner.

Part A: That rescinded offer in the news

Last week, a story of outrage made the rounds. The capsule version is this: A philosopher is offered a job at a small teaching school. She tries to negotiate for the job. She then gets immediately punished for negotiating, by having the offer rescinded.

This story first broke on a philosophy blog, then into Inside Higher Ed, and some more mainstream media, if that’s what Jezebel is. There are a variety of other posts on the topic including this, and another by Cedar Reiner.

Some have expressed massive shock and appall. However, after reading the correspondence that caused the Dean to rescind the job offer, I’m not surprised at all. After initial conversations, the candidate wrote to the Dean:

As you know, I am very enthusiastic about the possibility of coming to Nazareth. Granting some of the following provisions would make my decision easier.

1) An increase of my starting salary to $65,000, which is more in line with what assistant professors in philosophy have been getting in the last few years.

2) An official semester of maternity leave.

3) A pre-tenure sabbatical at some point during the bottom half of my tenure clock.

4) No more than three new class preps per year for the first three years.

5) A start date of academic year 2015 so I can complete my postdoc.

I know that some of these might be easier to grant than others. Let me know what you think.

Here is what the Dean thought, in her words:

Thank you for your email. The search committee discussed your provisions. They were also reviewed by the Dean and the VPAA. It was determined that on the whole these provisions indicate an interest in teaching at a research university and not at a college, like ours, that is both teaching and student centered. Thus, the institution has decided to withdraw its offer of employment to you.

Thank you very much for your interest in Nazareth College. We wish you the best in finding a suitable position.

There has been a suggestion of a gendered aspect. That viewpoint is expressed well here, among other places. (There doesn’t seem to be a pay equity problem on this campus, by the way.) I wholly get the fact that aggressive negotiation has been seen as a positive trait for men and a negative trait for women. I think it is possible that gender played a role, but in my view, the explanation offered by the Dean is the most parsimonious one. (Now, my opinion will be dismissed by some because of my privilege as a tenured white dude. Oh well.) Given the information that we’ve been provided, and interpreted in light of my experiences at a variety of teaching campuses, I find the “fit” explanation credible, even if it’s not what I would have done.

A job offer is a job offer, and once an offer is made the employer should stand behind the offer. Then again, if some highly extraordinary events unfold before an agreement is reached, the institution can rescind the job offer. In this circumstance, is the candidate’s email highly extraordinary?

Did this start at “negotiation” communicate so many horrible things about the candidate that the institution should have pulled its offer? The Dean’s answer to that question was, obviously, “Yes.”

I would have answered “no.” Many others have done the yeoman’s blog work of explaining exactly how and and why that was the wrong answer to the question. I’m more interested in attempting to crawl inside the minds of the Dean and the Department that withdrew the offer. What were they thinking?

The blog that first broke this story called these items “fairly standard ‘deal-sweeteners.’” I disagree. If I try to place myself in the shoes of the Dean and the Department, then this is how I think I might have read that request:

I am not sure if I really want this position. If you are willing to stretch your budget more than you have for any other job candidate in the history of the college, then I might decide to take the job, because accepting it is not an easy decision.

1) I realize that your initial salary offer was about what Assistant Professors make at your institution, but I want to earn 20% more, as much as your Associate Professors, because that’s what new faculty starting at research universities get.

2) I’d know that 6 months of parental leave is unofficial policy and standard practice, but I want it in writing.

3) I’d like you to hire adjuncts for an extra sabbatical before I come up for tenure. By then I’m sure I’ll need a break from teaching, even though everybody else waits until after tenure to take a sabbatical.

4) Before I take this special extra sabbatical, I want an easier teaching schedule than everybody else in my department.

5) I want to stay in my postdoc for an extra year, because I’d rather do more research somewhere else than teach for you. I realize that you advertised the position to fill teaching needs, but you can hire an adjunct.

While some of these requests are the kind that I’d expect to be fulfilled by a research institution, I’m hoping that you are able to treat me like a professor from a research institution. Now that you’ve offered me this teaching job, I want my teaching obligations to be as minimal as possible. Let me know what you think.

And the Dean did exactly that: she let her know what she thought. I’m not really joking: that’s really how I think it could be seen, inside the context of a teaching- and student-centered institution.

Here is a more unvarnished version of what I imagine the Dean was thinking:

Holy moly! Who do you think we are? Don’t you realize that we want to hire you to teach? I didn’t pull the salary out of thin air, and it was aligned with what other new Assistant Professors earn here. And if you want to teach here, why the heck do you want to stay in your postdoc which presumably pays less money? If you wanted to stay in your for 18 months earning a postdoc salary, instead of coming to teach for us at a faculty-level salary, then why would you even want this job at all? Also, didn’t you realize that we advertised for the position to start this year because we need someone to teach classes in September? If you have such crazy expectations now, then I can only imagine what a pain in the butt you might be for us after you get tenure. I think it’s best if we dodge this bullet and you can try to not teach at a different university. We’re looking for someone who’s excited about teaching our students, and not as excited about finding ways to avoid interacting with them.

The fact remains that the candidate is actually seeking a teaching-centered position. However, she definitely was requesting things that an informed candidate would only ask from a research institution. I don’t think that she necessarily erred in making oversized requests, but her oversized requests were for the wrong things. They are focused on research, and not on teaching. While it might be possible that all of those requests were designed to improve the quality of instruction and the opportunities to mentor students, it clearly didn’t read that way to the Dean. We know it didn’t read that way, because the Dean clearly wrote that she thought the candidate was focused too heavily away from teaching and students. I’m not sure if that’s true, but based on the email, that perspective makes a heckvualotta sense to me.

I’d would be more inclined to chalk the unwise requests to some very poor advice about how to negotiate. I’d would have given the candidate a call and try to figure out her reasons, and if the answers were student-centered, then I’d continue the negotiation. But I can see how a reasonable Dean, Department, and Vice President of Academic Affairs could read that email and decide that the candidate was just too risky.

New tenure-track faculty hires often evolve into permanent commitments. You need to make the most of your pick. Hiring a dud is a huge loss, and it pays to be risk averse. If someone reveals that they might be a dud during the hiring process, the wise course of action is to pick someone who shows a lower probability of being a dud. However, once an offer is made, the interview is over.

But according to Nazareth College, this candidate showed her hand as a total dud, and a massive misfit for institutional priorities. Though I wouldn’t have done it, I have a hard time faulting them for pulling the offer. If they proceeded any further, they would have taken the chance that they’d wind up with an enthusiastic researcher who would have been avoiding students at every opportunity. Someone who might want to bail as soon as starting. Or maybe someone who got a better job while on the postdoc and not show up the next year. The department only has four tenure-track faculty, and would probably like to see as many courses taught by tenure-line faculty as possible.

Having worked in a few small ponds like Nazareth, I don’t see the outrageousness of these events. We really have no idea, though, because there is a lot of missing context. But we know that the Dean ran this set of pie-in-the-sky requests by the Department and her boss. They talked about it and made sure that they weren’t going to get into (legal) hot water and also made sure that they actually wanted to dump this candidate. It’s a good bet that the Department got this email and said, “Pull up, pull up! Abort!” They may have thought, “If we actually are lucky enough to fill another tenure-track line, we don’t want to waste it on someone who only wants to teach three preps before taking a pre-tenure sabbatical while we cover their courses.” I don’t know what they were thinking, of course, but this seems possible.

Karen Kelsky pointed out that offers are rescinded more often at “less prestigious institutions.” She’s definitely on to something. Less prestigious institutions have more weighty teaching loads and fewer resources for research (regardless of the cost of tuition). These are the kinds of institutions that are most likely to find faculty job candidates who are wholly unprepared for the realities of life on the job.

When an offer gets pulled, I imagine it’s because the institution sees that they’ve got a pezzonovante on their hands and they get out while they still can.

At teaching institutions, nobody wants a faculty member who shies away from the primary job responsibility: teaching.

In a research institution, how would the Dean and the Department feel if a job candidate asked the Dean for reduced research productivity expectations and a higher teaching load for the first few years? Wouldn’t that freak the Department out and show that they didn’t get a person passionate for research? Wouldn’t the Dean rethink that job offer? Why should it be any different for someone wanting to duck teaching at a teaching institution?

I don’t know what happened on the job interview, but that email from the candidate to the Dean is a huge red flag word embroidered with script that reads: “I don’t want to teach” and “I expect you to give me resources just like a research university would.” Of course everybody benefits when new faculty members get reassigned time to stabilize. But these requests were not just over the top, they were in orbit.

If I were the Dean at a teaching campus, what kinds of things would I want to see from my humanities job candidates? How about a guarantee for the chance to teach a specialty course? Funds to attend special conferences and funds to hire students as research assistants. Someone wanting to start early so that they could start curriculum development. Someone wanting a summer stipend to do research outside the academic year?

Here’s the other big problem I have with the narrative that has dogpaddled around this story. It’s claimed that the job offer was rescinded because she wanted to negotiate. But that’s not the case. The job candidate was not even negotiating.

Part B: What exactly is negotiation and how do you do it with a teaching institution?

A negotiation is a discussion of give and take. You do this for me, I do this for you. You give me the whip, and I’ll throw you the idol.

In the pulled offer at Nazareth College, the job candidate was attempting to “negotiate” like Satipo (the dude with the whip), but from other side of the gap.

What the Dean received from the candidate wasn’t even a start to a negotiation. It was, “Here is everything I want from you, how much can you give to me?” That is not a negotiation. A negotiation says, “Here are some things I’m interested in from you. If you give me these things, this is what I have to offer.”

How should this candidate have started the negotiation? Well, actually, the email should have been a request to schedule a phone conversation. What should the content of that conversation have been? How could the candidate have broached the huge requests (pre-tenure sabbatical, starting in 18 months, very few preps, huge salary)? By acknowledging that by providing these huge requests, huge output would come back.

“Once I get a contract for my second book, could you give me a pre-tenure sabbatical to write this book?”

“I’m concerned I won’t be able balance my schedule if I have too many preps early on. If you can keep my preps down to three per year, I’ll be more confident in my teaching quality and I should be able to continue writing manuscripts at the same time.”

“Right now, I am working on this exciting project during my postdoc, which is funded for another year. If it’s possible for me to arrive on campus after I finish my postdoc, this work will really help me create an innovative curriculum for [a course I will be teaching]. During this postdoc, I’d be glad to host some students from the college for internships and help them build career connections.” Of course, it’s very rare a teaching institution wants to wait a whole extra year. They want someone to teach, after all! It couldn’t hurt much to ask, if you phrase it like this, verbally.

“After running the numbers, I see that a salary of $65,000 is standard on the market for new faculty at sister institutions. But from what I’ve seen from the salary survey, this is well above the median salary for incoming faculty. If you can find the funds to bring me in at this salary, I’m okay if you trim back moving expenses. Being paid at current market rate in my field is important to me, and if you let me know what level of performance is tied to that level of compensation, I’ll deliver.”

By no means am I a negotiation pro. What I do know comes mostly from the classic book, “Getting to Yes.” The main point of this book is that “positional negotiation” is less likely to be successful. This approach involves opposite sides taking extreme positions and then finding a middle ground. Just like asking for a huge salary, and lots of reassigned time and easy teaching.

Getting to Yes explains how to do “principled negotiation.” In this case, you have a true negotiating partner in which you understand and respect one another’s interests. So, instead of haggling over salary like buying a used piece of furniture at a swap meet, you discuss the basis for the salary and what each of you will get out of it.

If you are asking for a reduced teaching load, then you explain what you will deliver with this reduced teaching load (higher quality teaching and more scholarship), and what the consequences will be if you don’t get it (potential struggle while teaching and fear that you won’t have time to do scholarship). And so on. The quotes I suggested above are what you’d expect to see in a principled negotiation. The book is a bit long but there are some critical ideas in there, and I’m really glad I read it before I negotiated my current position. When it was done, both I and the Dean thought we won, and we reached a fair agreement.

If you are in the position of receiving an academic job offer, negotiating for the best starting position is critical. You don’t have to be afraid of having the offer withdrawn as long as you’re negotiating in good faith. That mean you communicate an understanding the constraints and interests of your negotiating partner. And being sure that when you are ask for something, your reason is designed to fulfill the interests of your partner as much as yourself. So, asking for a bunch of different ways to get out of teaching responsibilities is a non-starter when your main job responsibility is teaching.

It’s not only acceptable to negotiate when you are starting an academic job, it’s expected. The worst lesson to take from this incident is Nazareth is that there is peril in negotiation. I suggest that the lesson is that you must negotiate. And, keep in mind that negotiation is a conversation and a partnership towards a common goal. Even when it comes to money, there is a common goal: You want to be paid enough that you’ll be happy and stay, and they want you to be paid enough that you’ll stay.

You won’t have anybody pull a job offer from you if you’re genuinely negotiating. It’s okay to ask for things that your negotiating partner can’t, or may not want to, deliver. However, what you ask for should reflect what you really truly want, and at the moment you’re asking, provide a clear rationale, so that you appear reasonable. If you’re interviewing for jobs, then I recommend picking up a copy of Getting to Yes.

I own my data, until I don’t.

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Science is in the middle of a range war, or perhaps a skirmish.

Ten years ago, I saw a mighty good western called Open Range. Based on the ads, I thought it was just another Kevin Costner vehicle. But Duncan Shepherd, the notoriously stingy movie critic, gave it three stars. I not only went, but also talked my spouse into joining me. (Though she needs to take my word for it, because she doesn’t recall the event whatsoever.)

The central conflict in Open Range is between fatcat establishment cattle ranchers and a band of noble itinerant free grazers. The free grazers roam the countryside with their cows in tow, chewing up the prairie wherever they choose to meander. In the time the movie was set, the free grazers were approaching extirpation as the western US was becoming more and more subdivided into fenced parcels. (That’s why they filmed it in Alberta.) To learn more about this, you could swing by the Barbed Wire Museum.

The ranchers didn’t take kindly to the free grazers using their land. The free grazers thought, well, that free grazing has been a well-established practice and that grass out in the open should be free.

If you’ve ever passed through the middle of the United States, you’d quickly realize that the free grazers lost the range wars.

On the prairie, what constitutes community property? If you’re on loosely regulated public land administered by the Bureau of Land Management, then you can use that land as you wish, but for certain uses (such as grazing), you need to lease it from the government. You can’t feed your cow for free, nowadays. That community property argument was settled long ago.

Now to the contemporary range wars in science: What constitutes community property in the scientific endeavor?

In recent years, technological tools have evolved such that scientists can readily share raw datasets with anybody who has an internet connection. There are some who argue that all raw data used to construct a scientific paper should become community property. Some have the extreme position that as soon as a datum is collected, regardless of the circumstances, it should become public knowledge as promptly as it is recorded. At the other extreme, some others think that data are the property of the scientists who created them, and that the publication of a scientific paper doesn’t necessarily require dissemination of raw data.

Like in most matters, the opinions of most scientists probably lie somewhere between the two poles.

The status quo, for the moment, is that most scientists do not openly disseminate their raw data. In my field, most new papers that I encounter are not accompanied with fully downloadable raw datasets. However, some funding agencies are requiring the availability of raw data. There are a few journals of which I am aware that require all authors to archive data upon publication, and there are many that support but do not require archiving.

The access to other people’s data, without the need to interact with the creators of the data, is increasing in prevalence. As the situation evolves, folks on both sides are getting upset at the rate of change – either it’s too slow, or too quick, or in the wrong direction.

Regardless of the trajectory of “open science,” the fact remains that, at the moment, we are conducing research in a culture of data ownership. With some notable exceptions, the default expectation is that when data are collected, the scientist is not necessarily obligated to make these data available to others.

Even after a paper is published, there is no broadly accepted community standard that the data that resulted in the paper become public information. On what grounds do I assert this? Well, last year I had three papers come out, all of which are in reputable journals (Biotropica, Naturwissenschaften, and Oikos, if you’re curious). In the process of publishing these papers, nobody ever even hinted that I could or should share the data that I used to write these papers. This is pretty good evidence that publishing data is not yet standard practice, though things are slowly moving in that direction. As evidence, I just got an email from Oikos as a recent author asking me to fill out a survey to let them know how I feel about data archiving policies for the journal.

As far as the world is concerned, I still own the data from those three papers published last year. If you ask me for the data, I’d be glad to share them with you after a bit of conversation, but for the moment, for most journals it seems to be my choice. I don’t think any of those three journals have a policy indicating that I need to share my dataset with the public. I imagine this could change in the near future.

I was chatting with a collaborator a couple weeks ago (working on “paper i”) and we were trying to decide where we should send the paper. We talked about PLOS ONE. I’ve sent one paper to this journal, actually one of best papers. Then I heard about a new policy of the journal to require public archiving of datasets from all papers published in the journal.

All of sudden, I’m less excited about submitting to this journal. I’m not the only one to feel this way, you know.

Why am I sour on required data archiving? Well, for starters, it is more work for me. We did the field and lab work for this paper during 2007-2009. This is a side project for everybody involved and it’s taken a long time to get the activation energy to get this paper written, even if the results are super-cool.

Is that my fault that it’ll take more work to share the data? Sure, it’s my fault. I could have put more effort into data management from out outset. But I didn’t, as it would have been more effort, and kept me from doing as much science as I have done. It comes with temporal overhead. Much of the data were generated by an undergraduate researcher, a solid scientist with decent data management practices. But I was working with multiple undergraduates in the field in that period of time, and we were getting a lot done. I have no doubts in the validity of the science we are writing up, but I am entirely unthrilled about cleaning up the dataset and adding the details into the metadata for the uninitiated. And, our data are a combination of behavioral bioassays, GC-MS results from a collaborator, all kinds of ecological field measurements, weather over a period of months, and so on. To get these numbers into a downloadable and understandable condition would be, frankly, an annoying pain in the ass. And anybody working on these questions wouldn’t want the raw data anyway, and there’s no way these particular data would be useful in anybody’s meta analysis. It’d be a huge waste of my time.

Considering the time it takes me to get papers written, I think it’s cute that some people promoting data archiving have suggested a 1-year embargo after publication. (I realize that this is a standard timeframe for GenBank embargoes.) The implication is that within that one year, I should be able to use that dataset for all it’s worth before I share it with others. We may very well want to use these data to build a new project, and if I do, then it probably would be at least a year before we head back to the rainforest again to get that project done. At least with the pace of work in my lab, an embargo for less than five years would be useless to me.

Sometimes, I have more than one paper in mind when I am running a particular experiment. More often, when writing a paper, I discover the need to write different one involving the same dataset (Shhh. Don’t tell Jeremy Fox that I do this.) I research in a teaching institution, and things often happen at a slower pace than at the research institutions which are home to most “open science” advocates. Believe it or not, there are some key results from a 15-year old dataset that I am planning to write up in the next few years, whenever I have the chance to take a sabbatical. This dataset has already been featured in some other papers.

One of the standard arguments for publishing raw datasets is that the lack of full data sharing slows down the progress of science. It is true that, in the short term, more and better papers might be published if all datasets were freely downloadable. However, in the long term, would everybody be generating as much data as they are now? Speaking only for myself, if I realized that publishing a paper would require the sharing of all of the raw data that went into that paper, then I would be reluctant to collect large and high-risk datasets, because I wouldn’t be sure to get as large a payoff from that dataset once the data are accessible.

Science is hard. Doing science inside a teaching institution is even harder. I am prone isolation from the research community because of where I work. By making my data available to others online without any communication, what would be the effect of sharing all of my raw data? I could either become more integrated with my peers, or more isolated from them. If I knew that making my data freely downloadable would increase interactions with others, I’d do it in a heartbeat. But when my papers get downloaded and cited I’m usually oblivious to this fact until the paper comes out. I can only imagine that the same thing could happen with raw data, though the rates of download would be lower.

In the prevailing culture, when data are shared, along with some other substantial contributions, that’s standard grounds for authorship. While most guidelines indicate that providing data to a collaborator is not supposed to be grounds for authorship, the current practice is that it is grounds for authorship. One can argue that it isn’t fair nor is it right, but that is what happens. Plenty of journals require specification of individual author contributions and require that all authors had a substantial role beyond data contribution. However, this does not preclude that the people who provide data do not become authors.

In the culture of data ownership, the people who want to write papers using data in the hands of other scientists need to come to an agreement to gain access to these data. That agreement usually involves authorship. Researchers who create interesting and useful data – and data that are difficult to collect – can use those data as a bargaining chip for authorship. This might not be proper or right, and this might not fit the guidelines that are published by journals, but this is actually what happens.

This system is the one that  “open science” advocates want to change. There are some databases with massive amounts of ecological and genomic data that other people can use, and some people can go a long time without collecting their own data and just use the data of others. I’m fine with that. I’m also fine with not throwing my data in to the mix.

My data are hard-won, and the manuscripts are harder-won. I want to be sure that I have the fullest opportunity to use my data before anybody else has the opportunity. In today’s marketplace of science, having a dataset cited in a publication isn’t much credit at all. Not in the eyes of search committees, or my Dean, or the bulk of the research community. The discussion about the publication of raw data often avoids tacit facts about authorship and the culture of data ownership.

To be able to collect data and do science, I need grant money.

To get grant money, I need to give the appearance of scientific productivity.

To show scientific productivity, I need to publish a bunch of papers.

To publish a bunch of papers, I need to leverage my expertise to build collaborations.

To leverage my expertise to build collaborations, I need to have something of quality to offer.

To have something of quality to offer, I need to control access to the data that I have collected. I don’t want that to stop after publication.

The above model of scientific productivity is part of the culture of data ownership, in which I have developed my career at a teaching institution. I’m used to working amicably and collaboratively, and the level of territoriality in my subfields is quite low. I’ve read the arguments, but I don’t see how providing my data with no strings attached would somehow build more collaborations for me, and I don’t see how it would give me any assistance in the currency that matters. I am sure that “open science” advocates are wholly convinced that putting my data online would increase, rather than constrict opportunities for me. I am not convinced, yet, though I’m open to being convinced. I think what will convince me is seeing a change in the prevailing culture.

There is one absurdity to these concerns of mine, that I’m sure critics will have fun highlighting. I doubt many people would be downloading my data en masse. But, it’s not that outlandish, and people have done papers following up on my own work after communicating with me. I work at a field site where many other people work; a new paper comes out from this place every few days. I already am pooling data with others for collaborations. I’d like to think that people want to work with me because of what I can bring to the table other than my data, but I’m not keen on testing that working hypothesis.

Simply put, in today’s scientific rewards system, data are a currency. Advocates of sharing raw data may argue that public archiving is like an investment with this currency that will yield greater interest than a private investment. The factors that shape whether the yield is greater in a public or private investment of the currency of data are complicated. It would be overly simplistic to assert that I have nothing to lose and everything to gain by sharing my raw data without any strings attached.

While good things come to those who are generous, I also have relatively little to give, and I might not be doing myself or science a service if I go bankrupt. Anybody who has worked with me will report (I hope) that am inclusive and giving with what I have to offer. I’ve often emailed datasets without people even asking for them, without any restrictions or provisions. I want my data to be used widely. But even more, I want to be involved when that happens.

Because I run a small operation in a teaching institution, my research program experiences a set of structural disadvantages compared to colleagues at an R1 institution. The requirement to share data levies the disadvantage disproportionately against researchers like myself, and others with little funding to rapidly capitalize on the creation of quality data.

To grow a scientific paper, many ingredients are required. As grass grows the cow, data grows a scientific paper.

In Open Range, the resource in dispute is not the grass, but the cows. The bad guy ranchers aren’t upset about losing the grass, they just don’t want these interlopers on their land. It’s a matter of control and territoriality. At the moment, the status quo is that we run our own labs, and the data growing in these labs are also our property.

When people don’t want to release their data, they don’t care about the data itself. They care about the papers that could result from these data. I don’t care if people have numbers that I collect. What I care about is the notion that these numbers are scientifically useful, and that I wish to get scientific credit for the usefulness of these numbers. Once the data are public, there is scant credit for that work.

It takes plenty of time and effort to generate data. In my case, lots of sweat, and occasionally some venom and blood, is required to generate data. I also spend several weeks per year away from my family, which any parent should relate with. Many of the students who work with me also have made tremendous personal investments into the work as well. Generating data in my lab often comes at great personal expense. Right now, if we publicly archived data that were used in the creation of a new paper, we would not get appropriate credit in a currency of value in the academic marketplace.

When a pharmaceutical company develops a new drug, the structure of the drug is published. But the company has a twenty year patent and five years of exclusivity. It’s widely claimed – and believed – that without the potential for recouping the costs of work in developing medicines that pharmaceutical companies wouldn’t jump through all the regulatory hoops to get new drugs on the market. The patent provides incentive for drug production. Some organizations might make drugs out of the goodness of their hearts, but the free market is driven by dollars. An equivalent argument could be wagered for scientists wishing for a very long time window to reap the rewards of producing their own data.

In the United States, most meat that people consume doesn’t come from grass on the prairie, but from corn grown in an industrial agricultural setting. Likewise, most scientific papers that get published come from corn-fed data produced by a laboratory machine designed to crank out a high output of papers. Ranchers stay in business by producing a lot of corn, and maximizing the amount of cow tissue that can be grown with that corn. Scientists stay in business by cranking out lots of data and maximizing how many papers can be generated from those data.

Doing research in a small pond, my laboratory is ill equipped to compete with the massive corn-fed laboratories producing many heads of cattle. Last year was a good year for me, and I had three papers. That’s never going to be able to compete with labs at research institutions — including the ones advocating for strings-free access to everybody’s data.

The movement towards public data archiving is essentially pushing for the deprivatization of information. It’s the conversion of a private resource into a community resource. I’m not saying this is bad, but I am pointing out this is a big change. The change is biggest for small labs, in which each datum takes a relatively greater effort to produce, and even more effort to bring to publication.

So far, what I’ve written is predicated on the notion that researchers (or their employers) actually have ownership of the data that they create. So, who actually owns data? The answer to that question isn’t simple. It depends on who collected it, who funded the collection of the data, and where the data were published.

If I collect data on my own dime, then I own these data. If my data were collected under the funding support of an agency (or a branch of an agency) that doesn’t require the public sharing of the raw data, then I still own these data. If my data are published in a journal that doesn’t require the publication of raw data, I still own these data.

It’s fully within the charge of NIH, NSF, DOE, USDA, EPA and everyone else to require the open sharing of data collected under their support. However, federal funding doesn’t necessarily necessitate public ownership (see this comment in Erin McKiernan’s blog for more on that.) If my funding agency, or some federal regulation, requires that my raw data be available for free downloads, then I no longer own these data. The same is true if a journal has a similar requirement. Also, if I choose to give away my data, then I no longer own them.

So, who is in a position to tell me when I need to make my data public? My program officer, or my editor.

If you wish, you can make it your business by lobbying the editors of journals to change their practices, and you can lobby your lawmakers and federal agencies for them to require and enforce the publication of raw datasets.

I think it’s great when people choose to share data. I won’t argue with the community-level benefits, though the magnitude of these benefits to the community vary with the type of data. In my particular situation, when I weigh the scant benefit to the community relative to the greater cost (and potential losses) to my research program, the decision to stay the course is mighty sensible.

There are some well-reasoned folks, who want to increase the publication of raw datasets, who understand my concerns. If you don’t think you understand my concerns, you really need to read this paper. In this paper, they had four recommendations for the scientific community at large, all of which I love:

  1. Facilitate more flexible embargoes on archived data
  2. Encourage communication between data generators and re-users
  3. Disclose data re-use ethics
  4. Encourage increased recognition of publicly archived data.

(It’s funny, in this paper they refer to the publication of raw data as “PDA” (public data archiving), but at least here in the States, that acronym means something else.)

And they’re right, those things will need to happen before I consider publishing raw data voluntarily. Those are the exact items that I brought up as my own concerns in this post. The embargo period would need to be far longer, and I’d want some reassurance that the people using my data will actually contact me about it, and if it gets re-used, that I have a genuine opportunity for collaboration as long as my data are a big enough piece. And, of course, if I don’t collaborate, then the form of credit in the scientific community will need to be greater than what happens now, which is getting just cited.

The Open Data Institute says that “If you are publishing open data, you are usually doing so because you want people to reuse it.” And I’d love for that to happen. But I wouldn’t want it to happen without me, because in my particular niche in the research community, the chance to work with other scientists is particularly valuable. I’d prefer that my data to be reused less often than more often, as long as that restriction enabled me more chances to work directly with others.

Scientists at teaching institutions have a hard time earning respect as researchers (see this post and read the comments for more on that topic). By sharing my data, I realize that I can engender more respect. But I also open myself up to being used. When my data are important to others, then my colleagues contact me. If anybody feels that contacting me isn’t necessary, then my data are not apparently necessary.

Is public data archiving here to stay, or is it a passing fad? That is not entirely clear.

There is a vocal minority that has done a lot to promote the free flow of raw data, but most practicing scientists are not on board this train. I would guess that the movement will grow into an establishment practice, but science is an odd mix of the revolutionary and the conservative. Since public data archiving is a practice that takes extra time and effort, and publishing already takes a lot work, the only way will catch on is if it is required. If a particular journal or agency wants me to share my data, then I will do so. But I’m not, yet, convinced that it is in my interest.

I hope that, in the future, I’ll be able to write a post in which I’m explaining why it’s in my interest to publish my raw data.

The day may come when I provide all of my data for free downloads, but that day is not today.

I am not picking up a gun in this range war. I’ll just keep grazing my little herd of cows in a large fragment of rainforest in Sarapiquí, Costa Rica until this war gets settled. In the meantime, if you have a project in mind involving some work I’ve done, please drop me a line. I’m always looking for engaged collaborators.

NSF mostly overlooked teaching institutions for Presidential Early Career Awards

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The PECASE awards from NSF were announced as a Christmas present for 19 scientists.

These Presidential Early Career Awardees are picked from the cream of the crop of the CAREER awards. The CAREER award emphasizes an integrated research and teaching career development plan. Hearty congratulations to all recipients! This should make the holiday even nicer.

This year’s recipients of NSF PECASE awards work at the following institutions. Undergraduate institutions are in bold.

Alabama

Arizona

Berkeley (x2)

BYU

Bowdoin

Caltech

Cornell

Georgia Tech

Louisville

Maryland

Minnesota

Princeton

Santa Barbara

Stanford

Univ. Puerto Rico at Cayey (classification here)

Texas

Virginia Tech

Wisconsin

According to NSF:

Selection for this award is based on two important criteria:  1) innovative research at the frontiers of science and technology that is relevant to the mission of the sponsoring organization or agency, and 2) community service demonstrated through scientific leadership, education or community outreach.

This year, if you round up, then 11% of these PECASE awards from via NSF went to scientists at primarily undergraduate institutions. If you browse the institutions with CAREER awardees, it’s clear that this nonrandom subset has a bias against the awardees from teaching institutions. There are far more than 11% CAREER awardees from teaching institutions.

Is this bias based on merit? That’s an interesting question. How is merit quantified by NSF when picking PECASE awards from among the CAREER awards? I have no idea how to answer that question.

I don’t find any of this surprising, but thought that I should put this fact out there. Those of you are applying for CAREERs from teaching institutions have a good shot at getting one, but, well, don’t make the mistake of thinking you have a fair shot at the PECASE. For the two of you who cracked that very hard nut this year, congratulations!

Does being a “Jack-of-all-Trades” impede or facilitate an early-stage academic career?

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This is a guest post by Andrea Kirkwood. She is an Assistant Professor in the Faculty of Science at UOIT, and you can follow on twitter at @KirkwoodLab

Jack-of-all-Trades, Master of Nothing

Recently a topic near and dear to my heart came up on Twitter. Allison Barner (@algaebarnacle) was live-tweeting from the Western Society of Naturalists meeting, and posted the following tweet:

This tweet caught my attention, because it touched on an issue that caused some anxiety for me as I completed my doctoral degree. At the time, I thought my graduate school training was too broad, straddling several disciplines (ecology, phycology and microbiology) across very different systems (lakes vs. wastewater lagoons). Some may view this is a strength, bringing to mind the classical view of what a Doctor of Philosophy should be. Yet at the time I was completing my Ph.D. (circa 2002), I suspected that my skill-set was viewed as old-fashioned, and was being supplanted by the next-wave of sexy techniques. I also felt like I knew a little about a lot of things, but an expert in nothing. Sound familiar? I attempted to rectify this by choosing to do my first post-doc in a lab where I could learn a sexy technique (i.e., applying molecular methods in phylogeny and diversity assessment). Becoming adept at a special skill had to be the right move because several of my peers were getting faculty positions based on their “special skills”. You’re a quantitative ecologist, we’ll hire you! You use the latest molecular technique, we’ll hire you! It seemed that if you had a specific, timely skill-set, you were highly marketable. The message was that Jack-of-all-Trades (JOAT) need not apply.

Still, I wasn’t personally satisfied with just learning the latest sexy tools. When an opportunity came up to do something completely different for my next post-doc, I jumped at the chance. Not only would I get to work in a new system (rivers of the Canadian Rockies), but learn more about theoretical ecology. Here I was expanding my academic repertoire yet again to the detriment of specialization. One could blame my lengthy sojourn as a post-doc (5.5 years) on not having an obvious niche research area. Nonetheless, my academic breadth made it possible to apply to a broad swath of jobs, and end up on interview short-lists. Based on some feedback though, it was apparent that search committees either found it difficult to pigeon-hole my research area, or didn’t think I quite fit the specialization they were looking for.

I was very fortunate to be hired by a new university in Canada that didn’t have the luxury of hiring specialists. They needed someone who could teach a broad selection of biology courses, as well as have an applied research angle to fulfill their STEM mandate. This was an extremely rare kind of faculty hire at a Canadian research university. In the United States, this is apparently the typical hiring emphasis for small, teaching-focused universities as vouched by Terry McGlynn (@hormiga) on Twitter:

Although I breathed a sigh of relief upon securing a tenure-track faculty position, I then had to fret about the views of research-funding committees. I know several colleagues who were denied funding, in part, because they could not convince the reviewers they were expert enough. To add to my angst, the Great Recession had begun and the current government was slashing and burning research funding. Ironically, it was these dire funding circumstances that showcased the strengths of being an academic JOAT. I quickly discovered that I could access a broader pool of funding sources compared to my specialist colleagues. I secured grants in ecology, conservation, and biotechnology. Now one could argue this approach allows funding agencies to direct your research program (i.e., tail wagging the dog), which is true to some extent. Ultimately, the researcher has to decide to what degree they will chase money this way, and perhaps only use this funding model during the lean years. In my mind, if it can keep your lab running and let you and your students continue to do science, it certainly has its merits.

So admittedly up to this point, I have provided a narrative that asserts my credentials as a card-carrying JOAT. What does that mean exactly? Am I really a Master of Nothing? The very nature of grad school is to become a specialist at something, at least compared to the general population. Along the way, grad students and post-docs acquire specialized skills in their fields, some more than others. Serendipitously, I became a leading expert on Didymosphenia geminata (aka “Didymo” or “rock snot”) during my second post-doc, and will unabashedly credit D. geminata for saving my career (a blog entry for another day). Does this mean I lose the right to claim the JOAT label at all?

Brett Favaro (@brettfavaro) sums it up nicely by stating:

Brett cites an interesting opinion piece by Parsons (2012) in the Journal of Environmental Studies and Sciences that certainly extolls the virtues of a broad skill-set in an interdisciplinary field like Conservation. However, it is not enough to know about a lot of things, but to also have a deep understanding of them too. Thus, one needs to have a complement of specialized skills, perhaps at the “expert-lite” level and not necessarily “leading-expert” level. Would you accept your oncologist or cardiologist being “expert-lite” in your treatment options? Probably not, but I think this approach lends itself to disciplines such as ecology and environmental science, where a broad and somewhat deep skill-set can be an asset in research collaboration and communication.

Offering an intriguing new layer to this discussion on the JOAT phenomena in academia, Britt Koskella (@bkoskella) pondered on Twitter:

Britt cites an article by Wang et al. (2013) that assesses the role of gender in influencing career choices in STEM vs. non-STEM fields. The authors determined that individuals with high ability in both math and verbal skills tended not to pursue STEM careers. In contrast, individuals who had high math skills, but moderate verbal skills tended to choose STEM careers. This suggests that having a broad-skill set (i.e., being a JOAT) offers more career options, and thus an increased capacity to choose a career outside of STEM. What is also notable about these findings is that the group with high math and verbal ability included more females. This raises interesting questions about the underlying cause(s) of fewer women than men entering STEM careers. Is it ultimately about inherent freedom to choose a career path rather than ability?

Overall, I think it is clear that there is no clear-cut answer to the question posed in the title of this blog post. In my own personal experience, I can say that being an academic JOAT likely helped or hindered me at different points along my career path. Based on the unique experiences of every academic, I imagine there is a multitude of views on the JOAT phenomenon, and whether it matters or even exists. I think it exists, but is defined by perception on a sliding-scale.

References

Parsons, E. C. (2012). You’ll be a conservationist if…. Journal of Environmental Studies and Sciences, 1-2.

Wang, M. T., Eccles, J. S., & Kenny, S. (2013). Not Lack of Ability but More Choice Individual and Gender Differences in Choice of Careers in Science, Technology, Engineering, and Mathematics. Psychological Science, 24(5), 770-775.

Journal prestige and publishing from a teaching institution

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Finally. There are journals publishing quality peer-reviewed research, but leave it to the reader to decide whether a paper is sexy or important. Shouldn’t this be better than letting a few editors and reviewers reject work based on whether they personally think that a paper is important or significant?

This newish type of journal uses editors and reviewers to assure quality and accuracy. The biggie is PLoS ONE. A newer one on the block is PeerJ. Another one asked me to shill for them on this site.

The last few years have seen a relatively quick shift in scientific publishing models, and there has been a great upheaval in journals in which some new ones have become relatively prestigious (e.g., Ecology Letters) and some well-established journals have experienced a decline in relative rank (e.g., American Journal of Botany). These hierarchies have a great effect on researchers publishing from small ponds.

Publishing in selective journals is required to establish legitimacy. This is true for everybody. Because researchers in small teaching institutions are inherently legitimacy-challenged, then this is the population that most heavily relies on this mechanism of legitimacy.

Researchers in teaching institutions don’t have a mountain of time for research. Just think about all of the time that could be spent on genuine research, instead of time wasted in the mill of salesmanship that is required to publish in selective journals. (I also find that pitching research as a theory-of-the-moment to be one of the most annoying parts of the business.)

With new journals that verify quality but not the sexiness, we can hop off the salesmanship game and just get stuff published. Sounds great, right?

After all, the research that takes place at teaching institutions can be of high quality and significant within our fields. But, on average, we just don’t publish as much. That makes sense because our employers expect us to focus on teaching above all else.

Since we’re less productive, then every paper counts. We want to get our research out there, but we also need to make sure that every paper represents us well. What we lack for in quantity, we need to make up for in (perceived) quality.

How do people assess research quality? The standard measure is the selectivity of the journal that publishes the paper. It’s natural to think that a paper in American Naturalist (impact factor 4.7) is going to be higher quality than American Midland Naturalist (impact factor 0.6).

People make these judgments all the time. It might not be fair, but it’s normal.

And no matter how dumb people say it might be, no matter how many examples are brought up, assessments of ‘journal quality’ aren’t going away. No matter how much altmetrics picks up as another highly flawed measure of research quality, the name of the journal that publishes a paper really matters. That isn’t changing anytime soon.

The effect of paper on the research community is tied to the prestige of the venue, as well as the prestige of the authors. Fame matters. If any researcher – including those of us at teaching institutions – wants to build an influential research program, we’ve got to build up a personal reputation for high quality research.

Building a reputation for high quality research is not easy at all, but it’s even harder while based at a teaching institution. Just like having a paper in a prestigious journal is supposed to be an indicator of quality research, a faculty position at a well-known research institution is supposed to be an indicator of a quality researcher. Since our institutional affiliations aren’t contributing to our research prestige, we need to make the most of the circumstances to establish the credibility and status of the work that comes out of our labs.

If journal hierarchies didn’t exist, it would be really hard for researchers in lesser-known institutions, who may not publish frequently, to readily convince others that their work is of high quality. Good work doesn’t get cited just because it’s good. It needs to be read first. And work in non-prestigious journals may simply go unread if the author isn’t already well known.

If journal hierarchies somehow faded, it’s not as if the perception of research quality would evolve into some perfect meritocracy. There are lots of conscious and unconscious biases, aside from quality, that affect whether or not work gets into a fancy-pants journal, but it is true that people without a fancy-pants background still can publish in elite venues based on the quality of their work. This means that people without an elite background can gain a high profile based on merit, though they do need to persevere though the biases working against them.

If journals themselves merely published work but without any prestige associated with them, then it would be even more difficult for people without well-connected networks to have their work read and cited. It wouldn’t democratize access to science; it would inherently favor the scientists with great connections. At least now, the decisions of a small number of editors and reviewers can put science from an obscure venue into a position where a large audience will see it. On the other hand, publishing in a journal without any prestige, like PLoS ONE, will allow work to be available to a global audience, but actually read by very few.

If I want my work to be read by ecologists, then publishing it in a perfectly good journal like Oikos will garner me more readers than if I publish it in PLoS ONE. Moreover, people will look at the Oikos paper and realize that at some point in its life, there was a set of reviewers and an editor who agreed that the paper was not only of high quality but also interesting or sexy enough to be accepted. It wasn’t just done well, but it’s also useful or important to the field. That can’t necessarily be said of all PLoS ONE papers.

Not that long ago, I thought that these journals lacking the exclusivity factor were a great thing because it allowed everybody equal access to research. What changed my mind? The paper that I chose to place in PLoS ONEI chose to put a paper that I was really excited about in this journal. It was a really neat discovery, and should lead to a whole new line of inquiry. (Also, the editorial experience was great, the reviewers were very exacting but even-handed, and the handling editor was top notch.)

Since that paper has come out just over a year ago, there have been a number of new papers on this or a closely related topic. But my paper has not been cited yet, even though it really should have been cited. Meanwhile they’re citing my older, far less interesting and useful, paper on the same topic from 2002.

Why has nobody cited the more recent paper? Either people think that it’s not relevant, not high enough quality, or they never found it. (Heck, the blog post about it has been seen more times than the paper itself.) Maybe people found it and then didn’t read it because of the journal. It’s really a goddamn great paper. And it’s getting ignored because I put in PLoS ONE. I have very little doubt that if I chose to put it in a specialized venue like Insectes Sociaux or Myrmecological News, both good journals that are read by social insect biologists, that it would be read more heavily and have been cited at least a few times. This paper could have been in an even higher profile journal, because it’s so frickin’ awesome, but I chose to put it in PLoS ONE. Oh well, I’ve learned my lesson. There are some papers in that venue that get very highly cited, but I think most things in there just get lost.

I would love for people to judge a paper based on the quality of its content rather than the name of the journal. But most people don’t do this. And I’m not going to choose to publish in a venue that may lead people to think that the work isn’t interesting or groundbreaking even before they have chosen to (not) read it. I’ll admit to not placing myself on the front of reform in scientific publishing, even if I make all of my papers immediately and universally available. I have to admit that I’m apt to select a moderately selective venue when possible, because I am concerned that people see my research as not only legitimate but also worthwhile. I’m not worried that my stuff isn’t quite good, but I want to make sure it’s not done in vain. Science is a social enterprise, and as a working scientist I need to put my work into the conversation.

So, teaching is for people who have imposter syndrome?

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Isn’t it a bummer when your research is founded on an invalid premise? This can’t be a good moment for a researcher whose work was featured in Science online. This article would be just silly, if it didn’t take itself so seriously while also being offensive.

As represented in this Science “careers” article, the project was designed to understand what might cause scientists to change their professional ambitions from a tenure-track position at a major research university to, well, something other than a tenure-track position at a major university.

Apparently, that change in career ambition is some kind of flaw in performance, as the study reported these students as “downshifters.”

Apparently, a tenure-track position at a research university is “faster” than other jobs that doctoral students take. According to the study — or at least interpreted by the author of the Science article — a teaching position or policy job is is slower than a running a research lab. Maybe that’s what some tenure-track faculty at R1 institutions might think, but that doesn’t make it true.

Is it just me, or is the notion that deciding against a tenure-track position at research institution is a “downshift” is a load of crap? If you’re designing a study with this as a presumption, then isn’t that going to result in confirmation bias?

If we decide to choose an equally ambitious path in a different direction than the PI of the study, then why is it that we are labeled as having downshifted our expectations of ourselves?

In grad school, at some point, I decided that I didn’t want a job at a research institution. The job that I ended up taking, at a primarily teaching institution, is not any easier and not any slower than running an R1 lab. It’s not easier, it’s just different. There’s a good argument to be made that, after I chose against an R1 job, that I’m running harder and faster than a PI at an R1 institution.

According to this study, I’d be a downshifter. That judgment of me gives me some indigestion.

Moving into a tenure-track position at a research institution is often considered the default route for doctoral students, even if the bulk do not end up in such a position. If a doctoral student decides in the middle of grad school that she wants to pursue a different path, how is this shifting down one’s expectations? How is it that downgrading one’s expectations?

Here’s how the study identified what a “downshifter” is and what she found, as I read the article in Science careers:

The authors interviewed a whole bunch of doctoral students at one university. Only about 25% 33% had a goal of working in a tenure-track position at a major research university. (I found this rather surprising, and a form of good news, actually. Do their advisors know this?!) Of the entire pool, less than ten percent initially had an ambition to become a professor in a tenure-track position, but then changed their minds.  These were the “downshifters.” (There were gender disparities, with fewer women wanting the R1 jobs and more women who chose to against the more-exalted path.)

So, here’s what I see in these data: 75% 66% of grad students don’t want to become R1 professors. During grad school, 10% change their mind and don’t want to become R1 professors. These “downshifters” are more likely to be suffering from imposter syndrome, as it was measured in the study, and the gender disparity results in more women changing their minds about their career goals.

Note: Before going to press with this piece, I corresponded with the PI of the study. She didn’t want to write a response to be included in the original post, but she did clarify some numbers. She wrote:

As far as the numbers go – currently 22.5% of the women in my sample and 27% of men aspire to tenure-track professorships with an emphasis on research. 40% of the students have either changed or seriously considered changing career goals while in graduate school, but only 23% have actually changed. 11% of women and 6% of men were classified as “downshifters” because they shifted from professor with an emphasis on research to one of the 11 other categories. That means that *more* than that 22.5 and 27% originally aspired to the TT – about 1/3.

The take-home message is, then, that if imposter syndrome is causing a leak in the so-called pipeline, where the small fraction of Ph.D. students who want a so-called “fast” job decreases even more when they have imposter syndrome, which disproportionately affects women.

Maybe if we stopped portraying the tenure-track positions at research institutions as the idealized goal of grad school, then perhaps we wouldn’t be so worried about driving people away from academia and research? These gender disparities are real, and very concerning, and by continuing to up the stakes about how special and important R1 faculty jobs are, we’re not helping the problem.

This was not a brief rant, but it was summed up by a colleague of mine in just a few, less testy, words:

In all fairness to the PI of the study, she told me that she had no editorial power over what was published in Science careers. I’m sure the author didn’t do the PI any favors in how he represented her work, and that’s why I offered her the opportunity to clarify and rebut before going to press. She declined to offer a specific rebuttal, but did indicate that both the Science piece and this post itself were not fairly representative of her work or her views.

She did send me a link that represents her views and reassured me that the use of the term downshifter “is not meant normatively in any way and instead to capture the issue as it has been addressed in previous literature.”

Is using the term downshifter acceptable as long as it’s used only because other people have in the literature? Doesn’t the apparently broad use of this term in the literature suggest that this entire line of investigation has some messed-up assumptions built into the hypotheses being tested? If all of the research on women leaking out of the pipeline originates with these kinds of value judgements, are the conclusions trustworthy?

How a research institution can mentor undergrads better than an undergraduate institution

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More doctoral students emerge from small liberal arts colleges than from the undergraduate populace of research institutions.

This is a point of pride held by liberal arts colleges, that market themselves as the best place to go if you want to become a scientist receiving a Ph.D. from a big-name research institution. Demographically, they’re correct.

Are small teaching schools better equipped to train undergraduate researchers better than big research institutions? I don’t think so.

In practice, liberal arts schools are far better at producing high quality researchers, but it’s not because of any inherent property of liberal arts schools. Some could argue that the curriculum itself might matter – that’s a discussion for another time – I’ll spend the rest of this post thinking about the single reason that people identify about what makes liberal arts schools a special place for budding researchers.

Here is the standard reasoning: Teaching schools provide students with the opportunity to have close professional interactions with their professors. Students in labs in small teaching institutions benefit from direct mentorship from the PI, which will more likely result in a higher quality research experience, better insights into how to do research, and greater opportunities to own their own research projects, enabling them to present at major venues and eventual publication as undergraduates.

How true is the preceding paragraph? It’s a straight-up fact that students at small teaching campuses are more likely to do more original research of their own working with their PI. And, if an undergraduate arbitrarily selects a research lab to join, then they’d probably end up getting a better experience at a teaching institution.

But, though this trend is real, research institutions have tremendous potential for training undergraduates. Without providing any additional resources, any research institution can be a top-notch training ground for undergraduates. After all, there is nothing inherent about teaching institutions that makes them better at training researchers.

There is nothing magical about having the PI as your direct mentor that will make you a better researcher and help you get into a better grad school. Looking closely at what supposedly makes a teaching institution better for training undergraduate researchers – close involvement with the PI – I see a massive handicap.

All of the literature on research mentorship says that the relationship is most successful when the mentor is just a little above the mentee in research experience. Even though the PI is a better academic expert and has mentored more, the Ph.D. student and the postdoc are in a position to be more effective as mentors.

The best mentoring arrangement is a multi-level team, in which the early undergrad works with a senior undergraduate, who then works with a Ph.D. student, who works with a postdoc as well as the PI. The PI knows everyone personally, and spends some time with the undergrads, but the graduate students are the better formal mentors. (A colleague of mine at a research institution recently tried to kick one of her own undergrad researchers out of the lab, because she didn’t recognize her. That’s not good.)

I suppose a young PI can connect more easily with students, but as we get older, then the nature of the relationship evolves. Add on a few years, and the gap between the PI and the student grows. Even if the PI is affable, and might truly understand the perspectives and thoughts of the students, it would be silly to ignore the fact that our students can’t relate to us and that we can’t relate to our students, even if we were once in their position. No matter how much time I spend with my students, now matter how similar our backgrounds are, the fact of who I am limits my ability to serve as a model. I can do all the right things in the mentoring process, but if a grad student did all of the right things, it would be even better. (And for my students from underrepresented groups, having a mentor from the same group is particularly powerful.)

I really like most of my students. I enjoy their company, and over time some have become good friends of mine. But, let’s face it, there’s a big gap. I’m older, have a kid and am married, and we don’t have that many overlapping interests. While I try hard to be transparent, I recognize that I seem like an enigma in a bunch of ways. (For example, earlier this summer one of my students was totally surprised that I use torrents to watch a couple TV shows. He just thought this was outside my realm for some reason.) I didn’t go to grad school in the middle ages, but things have changed since I’ve been there, and this is true for anybody who is at least halfway to tenure. If I try to discuss grad school with my students, I’m not nearly as credible or powerful as the same information coming from a current graduate student.

My position of authority makes me a less influential mentor.

I don’t want to overgeneralize from my experience, but I doubt that I’m alone.

You might be thinking, “Do your students really have to relate to their mentor to have an excellent research experience, and move their career to the next level?” Not necessarily. But I think it really helps. Especially for students who aren’t able to visualize themselves as capable of excelling in graduate school, a proximate model is an essential part of the mentoring process. Having seen my undergrads interact with doctoral students on a regular basis, it’s clear to me that without this kind of opportunity, that my students would missing out, big time.

Having a student know that the path has been blazed in front of them by other students, like them, matters. If students see other students throw themselves into research with great passion, they are more likely to allow themselves to get that excited. Of course, the same was true for me. But now, I’m an old bald dude with kids, and I get really excited about research, but in a different way. I can’t serve as a model for my students, even if I tried.

While grad students might not have the same authority and skill set as the PI, they can offer things that the PI can’t. This is exactly why a multi-level mentoring scheme is the way to go. The PI can choose to become involved when it is wise, and step back and focus on other things when the grad student has things under control.

Research institutions have grad students, but this doesn’t mean that they deliver great research experiences for undergraduates. While the personnel are available for a multi-level mentoring system, in many labs the system is nonfunctional because undergrads are often treated as serfs. I know many R1 labs that that are exceptional for undergraduates who work with graduate student mentors. However, I’m aware of far more labs that do not focus on making sure that undergraduates have their own research experience and are able to focus on building their own academic identity. In general, undergraduates in research institutions that receive their own project (as a piece of their mentor’s work) are the exception rather than the norm.

As for the mass production of Ph.D. students from small liberal arts colleges, I would bet that the outcome is a done deal even before the students enroll in college. The social and economic class that produces doctoral students is the same caste that is able to send students to fancy private liberal arts schools. Yes, there are scholarships and financial aid. But even if you look at small liberal arts colleges that heavily emphasize economic and ethnic diversity, they simply can’t match the diversity of the nation’s populace because, simply, most people can’t afford it. As long as the average cost of a liberal arts college is more than average cost of research universities, of course a higher proportion of doctoral students will emerge from liberal arts colleges.

How do I get my own students a multi-level mentored experience? Well, I don’t have that happen inside my lab on a day-to-day basis. I may have Master’s students around, but I usually have undergrads that are more seasoned than my grad students. That experience helps, but the way I really bring in graduate student and postdoc mentors is by having my students conduct their research in a hub of collaborative activity during the summer at a field station: La Selva Biological Station, in Costa Rica. There, my students build strong relationships with scientists from all over with different levels of experience, and these bonds typically stay tight after they leave the field station. Sometimes their projects become collaborations with grad students and postdocs at other institutions. I like that a lot, for a bunch of reasons.

If multi-level mentoring is important for the success of undergraduates, then what does this mean for you?

If you’re in a research institution: Postdocs and grad students should become genuine mentors and give undergraduates the time and resources to have their own students, and supervise them properly.  Faculty at research institutions should support their lab members, not just in the process of research but also in the process of mentorship. Don’t exploit undergraduates as trained monkeys. If you want someone to be an unthinking data-generating machine, then hire a technician. If you take an undergraduate to do “research,” then do actual research with them. Your own research agenda is easily split up into several smaller questions. Hand one of those questions to your undergraduate researcher, and learn how to mentor them. Give them the same support that you expect to receive from your own research advisor. Yeah, it’s not easy, but it will pay off for both of you in the long run.

If you’re at a teaching institution: Seek routes for multi-level mentoring in the lab. At a minimum, the undergraduates with more than two years of experience in the lab should be given the chance to actively supervise new students. Ideally, you can develop relationships with colleagues in other institutions with graduate students and postdocs. Find a way for your undergrads to become friends with doctoral students. I don’t know how to make this happen, and it varies with institutional context and geography, but from where I sit, it’s an ingredient that really promotes success. (For starters, you can bring students to smaller national meetings where they can build relationships with the students of your colleagues.)

I don’t have a big specific solution to the problem, but recognizing the fact that we as faculty are inherently flawed mentors is a start, and recognizing that the lack of graduate students at teaching institutions isn’t a strength, but a weakness, of the mentorship process.

Model systems don’t work at teaching universities

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Many research strategies, developed inside large research institutions, don’t work well in small teaching-centered institutions.

One of these strategies, I suggest, is the use of a biological model system.

What do I mean by model systems? Any system, used in multiple laboratories, that has been optimized for a broad variety of lab investigations. I’m thinking of zebrafish, Drosophila, mice, C. elegans, E. coli, Arabidopsis, and honey bees. For the ant people, this includes Temnothorax.

It might seem counterintuitive that model systems can prevent you from getting research done in a teaching-centered institution. After all, these model systems were developed, in part, to make research easier and remove methodological barriers experienced by those working in a broad variety of organisms.

Even though model organisms are easy to work with, have lots of standardized methods, and a massive bank of potential collaborators, the costs and risks of working with a model system far outweigh the benefits and the opportunities.

My initial thought on this topic came from observation. I don’t see much research on model systems getting done within teaching institutions. I also have seen a variety of people, who work on model systems in teaching institutions, experience substantial challenges that have kept them from getting work done. Here’s a list of problems that I’ve seen crop up for those using model systems in teaching institutions.

  • The low-hanging fruit has been picked. In model systems, the things which are a combination of easy, obvious and trivial have already been done. That only leaves things which are either difficult, requiring particularly deep insight, or trivial.
  • Model systems have intense competition. In model systems, many labs compete rather than collaborate. A small lab in a teaching institution isn’t built to deal with this kind of competition. This doesn’t mean that you should avoid competition by working on minutia, but it also means that you should choose an avenue that has high pressure competition. As any ecologist will tell you, competition is an interaction which has a negative effect on both parties. (An economist will tell you the same thing, though it’s better for the consumer).
  • You might get scooped. The probability that someone is doing exactly what you are working on is higher in a model system.
  • Collaborating is difficult. The labs that run model systems typically have lots of routine methods happening like clockwork, run by technicians and students who have become specialized in the model system. If you’re working on this model system, you are not likely to be able to contribute anything of substance to a big lab, which could do the same thing much more quickly than you could offer. Collaboration is easier if you have a specific resource or skills that others want or need, and that’s harder from a teaching institution if you work in a model system.
  • You may become isolated. In the community of people working in model systems, if you have a small undergraduate lab you’re more likely to be overlooked by your peers. I’m not sure how or why this is the case, but it’s what I’ve observed on several occasions. Everyone I know who has worked on a model system wasn’t well integrated into the community of frenemies that works on the system. It’s hard to have a scientific impact if you are unrecognized by your peers.
  • The bar for evidence is very high. Peer review is more likely to be rough and demanding. People with model systems tend to be territorial, and especially if you’re not perceived as a competitor that can bite back with teeth, then at least one reviewer is likely to go the extra mile to find something wrong with what you’ve done.
  • The minimum publishable unit is greater. If you’re working in a model system, odds are that the big labs working in the field are able to heavily replicate their experiments, and sandwich multiple experiments together into a paper. This establishes a standard for a large volume of data in a paper. I’m not arguing that you should seek to publish as soon as you have enough data for any kind of publication. However, if you have a genuine, interesting, and cool finding and want to get it out for everybody to read, it’s a bummer to have others telling you that you that it’s ready for publication because you don’t have a mountain of data.
  • Maintaining colonies takes continuous effort. Mustering any kind of consistent student work in the lab is a success. You don’t want your success in long-term student research to be squandered on the maintenance of lab colonies of a model system. You want to work on a system in which students can dive in and collect actual data, rather than spending all of their time keeping critters fed, clean, reproductive and healthy. Also, you want to be free to disappear for long periods of time, and not chained to the lab to maintain your organisms.

So why would one ever work with a model organism? One great reason would be to provide lots of valuable hands-on laboratory experiences to students. But in my observation, this kind of approach is a poor recipe for completing original research in a teaching institution.

In my opinion, if we are giving students research training, that means we’re giving them research training. Which means that they’re doing research, and research is something that ends up in new findings that are shared with the scientific community. If you want to give a genuine research experience to students, then you need to regularly publish the work that you are doing. From my vantage point, it’s very difficult to do this while working with a model system in a teaching institution.

I have a couple other alternative explanations for the (perceived) phenomenon that researchers working on model systems in teaching institution have a harder time publishing their work. The first alternative is that I’m just wrong, and that my experiences or perceptions aren’t representative or accurate.

The second alternative is that less research is completed because of the use of strategies from R1 labs that don’t translate to a teaching institution. Most people at teaching institutions, who work on model systems, are continuing with their dissertation system, I think. The research approaches from a dissertation lab probably don’t translate well to a teaching institution. I haven’t spent much time inside the labs of teaching institution professors using model systems, so I don’t know how they run things on a day-to-day basis, and am not too sure about this.

Do you think that it’s hard to get research done on a model system at a teaching institution? Did you switch into one, or out of one, to get research done? Thinking about it?

What is the filter in NSF preproposal review?

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A while ago, I had a conversation with a colleague that really bugged me.

This scientist submitted a biology proposal to NSF this January, which wasn’t invited for a full submission. I understand that the bulk of preproposals aren’t invited, but the rationale in this situation was messed up.

The reviews were clear that the research agenda and approach was excellent, and that the PIs were academically prepared to conduct the research. My colleague was told that the thing that tanked the preproposal was the lack of preliminary data. The panel thought that there were inadequate resources or facilities that enabled the PIs to get the project done, and that the junior faculty PIs were not prepared to run the project. Keep in mind that this was a project that was proposed to take place with senior collaborators at large research organizations.

I thought the purpose of preproposal was for the PI to argue for the importance of the question and the validity of the approach. Those conditions were met, quite well, according to the reviews.

If you’re at a small institution without a huge research infrastructure, did you know that you need to dedicate verbiage into your preproposal to defend the infrastructure at your institution? I didn’t. My colleagues got dinged on this at the preproposal stage.

Did you know that if you proposed an ambitious but eminently do-able project, that you needed to explain your qualifications in detail at the preproposal stage?  I would have thought that that both of those issues would be something to deal with a full proposal rather than a preproposal.

To be clear: If the PI was a tenured professor employed by a large research institution, with access to more preliminary data, then it sounds like this proposal would have been given an invitation to submit a full proposal. In my view, this is an unfair bias against the PI, who wasn’t even given a full chance to propose a research project on account of institution size and seniority.

I was really mad when I heard about this – and I would have taken a few days to call down, before yelling at my program director. Am I off, or are these good reasons to triage a preproposal? They keep saying it’s about the concepts and experimental approaches (and broader impacts) at this stage, but it sounds like it’s just a regular NSF review.

I didn’t submit in the last round, but I am preparing a submission for the next round. I realize there are all kinds of great advice from Prof-Like Substance on how preproposals were handled in deed during this last round; I’m just wondering how much verbiage out of five pages you have to spend on things that aren’t the concepts, experiments and broader effects.

Applying for a faculty position at a teaching institution: the research statement

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Pretty much every faculty job application requires a Research Statement. It could be called something slightly different (research plan, statement of research interests), but they’re all pretty much the same.

The research statement that you send to a research university has to be fundamentally different from the one you send to a teaching institution.

When you’re applying for a job at a research university, your research statement should explain that your research is academically exciting, you are a current or future rockstar, you’ll bring in a more grant money than a minor deity, produce a ton of prestigious publications, train amazing doctoral students and your research fits well into strategic emphases within the department. I guess. I’ve never been on a search committee at a research university.

If you sent that that message to a teaching institution, you wouldn’t make the shortlist, because you wouldn’t fit in.

What messages do you need to send to a search committee at a teaching institution in your teaching statement?

  • Your research is academically exciting
  • Your research will provide extraordinary opportunities for undergraduates
  • You have a realistic notion about the amount of research you’ll get done
  • Your work will be successful outside the context of big labs you have worked in before
  • You can get external funds, but your program won’t fall apart without it
  • Your research is complementary with the department (you fill an open niche)

You don’t need to overtly state the above points, but they all should be things that an informed reader can easily infer.

Teaching institutions want to hire the best researchers that they can. However, departments don’t want to hire someone who they aren’t sure can’t run a successful research program while teaching the standard teaching load. They don’t even want to waste an interview slot on that kind of person.

You want to make sure that your application doesn’t communicate, by inclusion or omission, that you might be someone who can’t teach a lot while doing research with students at the same time. But most applicants lack that kind of experience. This is not a handicap. Search committees can’t use past performance to evaluate how someone will do research in a teaching faculty position. Instead, they can size up the attitude of the applicant. When you write about how you mentor and do research, does your approach look like something they think would be successful? Those things matter, a lot.

Your research statement is not the centerpiece of your application, but it is important for narrowing down the applicant pool. The two major elements that the search committee will be looking for in your research statement are:

  1. Your research fits the specialty that the department is looking for
  2. Your attitude towards research fits well within departmental norms

Unfortunately, you don’t really have any control over either of those two elements. But what you can do, for the second element, is to make sure that the attitude in your application fits what you know of the institution and what might be more generally applicable to many teaching campuses.

One approach that can work well is to build your research statement around a five-year plan. You don’t have to call it that, but it should communicate the scope of how much work you’ll do before you submit your tenure file at the start of your sixth year. If you project much further into the future about what your research goals and interests are, you’ll sound unrealistic. If you don’t look ahead for multiple years of research, then you’ll appear unfocused. You shouldn’t describe specific experiments, but you should describe lines of research, where they’ll be going, and what you expect to discover.

One focal point of your research statement needs to explain the role of undergraduates. What is your mentoring style? How you do identify and recruit students for your lab? What are your priorities for student development? How do you design projects so that there are meaningful roles for undergraduates? These things are important. The fact that you place undergraduate mentorship inside your research statement is, itself, an expression of your priorities.

Like all other parts of the application, you’ll be more successful if you honestly communicate your priorities and goals. This will not only result in the right institutions picking you for an interview, but it also will have the added benefit of making sure that you will be able to fulfill the expectations that you create.

You don’t have to think too big or claim to be running a research program that will transform your discipline. You just need to get pubs out once in a while with student coauthors, mentor undergraduates in research, and hopefully get a grant. Put yourself in that mindset while crafting your research statement and you are more likely to have an ethos that will fit with the departmental mindset.

Holding my own with the big boys and girls

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Coming from a teaching institution, I consistently push against the stereotype that I can’t be as good as someone from a big research institution.

Last week I had the pleasure of speaking in a symposium along with a couple of the most prominent people in tropical biology. There were other good, non-famous, people there too. It was great fun, and I gave a damn good talk, even though the topic was slightly outside my normal line of research. After the conference, a periodic collaborator of mine told me that I was able to “hold my own” with the high-powered crowd. This was clearly a form of congratulations. Keep in mind that this is a person who I’ve known for over fifteen years.

What I thought, and didn’t choose to say, is: “That’s a funny thing to mention. Of course I did. Why would you think otherwise? Would somebody think that I might not be able to?”

If I worked at a big research university, nobody would have ever congratulated me on “holding my own” in the presence of more esteemed scientists. I would just be expected to fit in. If anything, my presence in the symposium should indicate my enhanced stature, not a temporary ability to be acceptable in the presence of the great ones.

Why didn’t I tell my buddy that this compliment wasn’t a compliment? Why didn’t I say that he revealed his own bias, or his own low expectations?

I didn’t say anything because it wouldn’t do any good. I’d sound petulant. Some might say that I’d be looking for an insult where one was not intended. (Perhaps that what readers are thinking now.) Perhaps it would give the false impression that I’m unhappy.

I don’t see the remark about “holding my own” as a personal insult because I understand that it originated from a bias against my employer rather than against my work. This is especially the case because this colleague is in an entirely different subfield and I doubt he’s read many, if any, of my papers.

In that symposium, there were many people in my sub-subfield in attendance, who are quite familiar with my work. I don’t know what they were thinking, and I’m not going to ask. All I can do is continue to show again, and again, and continually, that I belong. Maybe one day, my colleagues won’t have to compliment me on holding my own with the big boys and girls.

Applying for faculty positions at teaching institutions: interpreting research culture and opportunities during an interview

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This is a follow-up to an earlier post on how to look at a teaching institution to find out if serious research is possible on campus. One main point was that you can’t diagnose whether or not you will be able to run your research program at a teaching school until you have some direct experience with the campus, like you’d get during an interview. So, in this post, I discuss how to handle plans for serious research during the interview.

When interviewing, keep in mind five main points about research at teaching schools:

  • The culture of research on a campus isn’t predictive of how much research that you personally will be able to get done.
  • Before you interview, you need to be able to envision what your research program will look like and how it will operate, so you can know if you will have what you need to flourish.
  • At most teaching campuses, you’ll have few to no specialists in your field. So you should market your research program not with the potential for scientific discovery, but instead with the great opportunities that will be created for students.
  • The more specific you describe your plans, the more opportunity that others have to identify a perceived mismatch.
  • Ignore advice on how to succeed on a particular campus unless it comes from someone who is very successful at that institution.

(A sixth point might be that you can throw all of the preceding ideas out the window, if you happen to be the proprietor of a blog about doing research in a teaching institution. I haven’t interviewed yet since starting this site; if I am able to land any interviews this year, this could be interesting.)

The best way to figure out whether or not you could run your research out of a particular campus is to have a series of long, frank conversations with the faculty in your prospective department.

Those conversations aren’t going to happen; you can’t be that frank and I doubt your interviewers will be either. Your interlocutors may be wholly frank, but it’s not always possible to tell if this is the case. Be honest, but omit unnecessary details.

Let’s say you arrive for your interview with a very specific idea of what your research program will look like, and what it will take to succeed. Or, let’s say you don’t have a specific idea but you are open to a variety of approaches.  In either scenario, you’ve got a problem during the interview. You won’t be able to spend the 1.5 days on campus smoking out all of the details about how you would run your research program.

There is a lot of ground to be covered in an interview. You’ll be expected to talk a lot, answering questions that the department has about you. Many of these questions will be about teaching, some will be about your research, and more about how you engage students in research. You also will be expected to ask a lot of questions. However, the majority of questions that you ask should be about teaching, because the institution is, after all, focused on teaching.

Remember that your ideas about research are probably very different from the people who have worked on this campus for a while. Your definition of being an active scholar is probably different from theirs. You lack the campus frame of reference. You can’t accurately perceive that frame of reference through a mere conversations. It actually takes years to figure out a campus.

You can attempt to construct a makeshift understanding of the campus research culture by reconciling what you hear and see on an interview with external information. You should be aware of what everyone is teaching, research specialties, and levels of research productivity. You can’t look at everyone’s CVs, but you can look at their websites and do a literature search before you go on an interview.

Then, you can search for consistencies and incongruent conceptions.

In my experience, the people who spend the most time talking about research during an interview are the ones that do the least amount of research. I’m not entirely sure why this is the case. Most people are excited to learn about your own research and see the interview as a learning opportunity, and this is true regardless of one’s research activity. Some people who aren’t serious researchers think that they are, and want to have a heart-to-heart talk about what it takes to do research on campus and how to overcome obstacles.

For the most part, the others in the interview are trying to convince you to take the job, assuming that you get the offer. So, they are working to make sure that you have a realistic view of the situation while also understanding the available opportunities and resources. This sales job by the department is based on their incomplete understanding of what your research program requires. The more the department knows about what you need, the more they can inform you about the advantages and drawbacks of this particular position. However, the more the department knows, the more likely they will identify ways in which the job won’t work for you.

Keep in mind that what a person chooses to discuss doesn’t communicate personal priorities. Most people are trying to provide interviewees with as much information as possible, in a positive light, to be able to help everyone make the best decision. Some might be emphasizing a sales job to convince an applicant to come, and others might be trying to be evaluative of the job candidate to see if they have the right answers. Everybody is different.

I have always made a point to ask a couple questions of many different people in the department, and it’s been very informative to see how responses vary among individuals. It might be “How do you find students to work in your research lab?” or “What is it like working with the grants office?” or something about the level and predictability of small-scale internal research funding.

Moreover, I often asked, “What is expected before coming up for tenure?” This is a totally reasonable question that everyone would expect to be asked, and it’s expected that you’d ask it several times with different people. This is an open-ended question that can help you identify individual priorities and perceptions of internal challenges. (Also, highly inconsistent answers are a very bad sign of a fractured institution.)

Be sure to listen to questions you received. For example, when I interviewed for my last job, nearly everyone asked me at one point or another, “What do you think about the idea of having a graduate program in the department?” It didn’t take long to identify the majority and minority factions in the department. This was an indicator of a division within the department that I learned about during the interview. It was complicated and I didn’t have all of the facts, but I saw that the role of research in the department was a fractious issue, and I went in with that concern in mind.

You’ll never get to understand the individual priorities that each professor has during a search. Most faculty are just looking for a colleague who can teach well, get research done with students, be effective, is easy to get along with, and won’t leave for another job. Some faculty might be concerned that junior faculty will raise the bar on research expectations. Some faculty will want new people to relieve them of teaching or service assignments of which they’ve grown weary. Others might want a friend. Too often, people want to have their own pet statistical consultants. These little quirks vary and you can’t really predict or control for them.

Regardless, you should realize this much: very few faculty at teaching institutions are actively excited about hiring junior faculty who are planning to have extremely robust research agendas. Most people at teaching campuses see those big research ambitions as a poor fit, and think that those people belong at R1 universities.

So, there is little to be gained by explaining that you have big research plans, if you have them. Of course you need to communicate that you will be getting research done with students, publishing, and plan to land outside grants. But you don’t want to make an overly big deal about research.

Why not? Having a research-serious departmentmate doesn’t really help any other faculty in the department. Some people might see it as a net drain, if the new researcher is taking away the best research students, buying time out of teaching and forsaking service for research. Even faculty who are strong researchers don’t have much incentive for bringing in additional researchers. They’ll just compete for limited resources from the Dean’s office and steal the limelight. (Those don’t matter for me, as the resources are already so limited that another mouth to feed can’t hurt me, and the research limelight on my campus is dim for all.)

Am I overstating the lack of interest in research in job candidates at teaching campuses? I don’t think so.

I’m as gung-ho for research as anybody on my campus. But whenever we get to make a new hire, is it important to me that this new person builds a productive research lab? Not really. I would like it because it would help enhance the overall research climate in the campus, but we have many more pressing immediate and long-term needs, dealing with the curriculum, departmental service, advising, vetting out jerks and finding someone who is truly student-centered. If a productive research lab run by a new hire isn’t my own priority in a search, it’s probably not a top priority of other reasonable people who aren’t working to actively promote research on teaching campuses.

Here’s another quirk of teaching campuses: Because research is mostly a solitary endeavor (because each campus typically has only one person in each specialty), then the professors on campus who talk a lot about research are prone to be seen as narcissists or out of touch with others. If you talk too much about your research during an interview, then you risk sounding like one of those narcissists.

If you’re asked about your research ideas or plans, give a 30-second summary. Elaborate when asked, but you shouldn’t be giving a reply that takes more than a minute or two unless you get clear verbal or nonverbal cues to continue. That’s true for almost every question, but it’s particularly important to remember to be brief about your research.

After you’ve done your best to understand the research culture during an interview, then you need figure out how, or if, you research can fit in on the campus. Remember that the absence of a research culture doesn’t preclude the establishment of a productive research program. It just means that your productive research program wouldn’t matter much to anybody, or it might even be threatening to others.

While all kinds of support is wonderful, it’s perfectly fine if your research program is greeted with ambivalence.

How can you tell whether an agenda for serious research will be greeted with antagonism? It’s not easy, and I got it wrong when I was interviewing.

At a distance, if you were to look at my current and previous departments, it would be easy to make the mistake in thinking that research would have been more possible in my old job. People in that department published more often, and there was a lot more talk about research. The university was able to give every professor a few thousand bucks per year for research, paid for travel to conferences, and there was a university-funded student research program in the summer. What was less obvious to the casual observer is that there was clear antagonism to big-time productivity. There was one big-league researcher in my department, and he kept all of his research almost secret.

At my current job, you might think that research is impossible. We have scant startup funds, very low rates of faculty publication, and no internal support for research or travel (though this year is an exception). However, a productive research program is far more possible in this job compared to my old one. Anybody who can build a highly productive research lab is more than welcome. That welcome doesn’t translate into more space or resources, but everyone is happy and that kind of thing is strongly encouraged. I didn’t realize this would be the case until I was well established into the job, and I’m very glad it has turned out this way.

How can you figure out if a teaching campus passes the anti-research smell test?

You need to pay attention to subtle cues, see how people talk about one another and their priorities.  If you don’t mind not getting a job offer from a place hostile to a highly productive lab, you could ask straight out, “How would you feel if I ended up getting big grants, reduced my teaching load down to two courses per semester and spend lots of time training research students and writing manuscripts?”

Or you could ask, “When is too much research a problem?” When approaching this issue, remember that you don’t want to be seen as overly optimistic, or naïve, or not interested in teaching.

In sum, you need to be all about teaching, and that makes it a tricky dance to learn about true research opportunities. Because every faculty member needs to be dedicated to teaching above all else, you need to communicate this priority in the interview. I’m all about teaching and my students, but I’m also all about research. That idea is really hard to communicate in a short interview.  You communicate your priorities with your words and your actions, and people expect them to match.

To be successful in research, you need to forge your own path. This is particularly true at teaching institutions.

To be continued: specific things to do, and specific things to avoid, throughout the job application process.

Applying for positions at teaching institutions: identifying opportunities for serious research

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Are you looking for a job at a teaching institution but and want to pursue an aggressive research agenda? Are you advising grad students or postdocs who might move into a job at a teaching school? Here are some opinions.

Let’s get two important points out of the way, that you should consider before applying for a job at a teaching school.

First, if you dislike teaching, don’t apply. This is in your own interest. As I’ve written before, if you’re going to move “up” from a job at a teaching university to a job at a research institution, you’ll have to be awesome, and to do that, you’ll have to be great at the job, including teaching. The people who leave teaching schools for research universities are the kind of people who get awards for their teaching. I have yet to see a person who doesn’t like teaching to be truly great at it, even though some have claimed it’s possible.

Second, if your primary goal is research fame, you’ll never become famous for your research while working out of a teaching institution, at least in the sciences. Even if you’re doing rockstar-level work, you won’t be a rockstar until you go elsewhere. And to move elsewhere, you need to be a great teacher. You can get excellent research done at a teaching school, but earning broad respect for your research is difficult, and fame is pretty much out of reach. A couple of my research heroes built their research careers teaching institutions. But are they broadly famous in their fields? Not as famous as they should be based on their achievements.

With those two caveats aside, let’s say that you’re focused on research and you are open to working at a teaching school. What do you need to know to figure out of a school can support your research agenda?

Before attacking that question, first let’s address a fundamental difference in culture and terminology between teaching schools and research institutions.

At most teaching-centered schools, “research” and “research active” means something different than you might expect. A typical “research-active” faculty member doesn’t necessarily publish often, and might not even be pursuing external funding. Someone with a reputation for being a strong researcher on campus might be unknown as a scholar off campus. At teaching schools, if you’re “research active,” that means that you’re doing scholarship and sharing it in some form, which in the sciences could mean training students in the lab, getting them into doctoral programs, and presenting research at local and undergraduate conferences.

What I just pointed out isn’t negative, or a dig against teaching campuses. I’m merely clarifying what “research” means at teaching campuses. If you don’t evaluate schools through this lens, you could be in for a rude surprise.

So why do teaching schools perceive research so differently than the broader research community? Isn’t research simply research? We all are used to the notion that standard measures of research success are publications, citations, and funding. Right?

At teaching institutions, it’s easy to lose focus from what happens in the scholarly community in your own field. People spend plenty of time teaching, and tenure decisions are based on what people on your campus think of you. It’s easy to focus your lens exclusively on campus culture. At any teaching campus, very few people have a well-established research presence in their international scholarly community. To do a great job, you just need to teach well, get a few pubs in minor journals, and sell yourself well on campus. It’s what most people do, and that’s perfectly fine. That’s the culture on many teaching campuses. Some of the top-notch scholars on a campus may produce little in the way of papers, but produce many new scholars. Sending many students off to good doctoral programs is, arguably, a bigger contribution to the field than publishing some papers on your own. (Is there an oversupply of scientists? Hell no.)

When you read job ads, draft your application, and interview on campus, remember the teaching school lens. If a job ad gives equal verbiage to the importance of teaching and research, and the need for excellence in both, then the be aware that teaching campuses use an entirely different measuring stick for research excellence than the academic community in general. You could be called “excellent” at research on a teaching campus but have very few publications. Most teaching schools, except some well-endowed privates, do not solicit external reviews of scholarship. (My campus actually forbids committees to contact off-campus experts for evaluating research in tenure packages!)

If you hear that that serious researchers are welcome, or that serious research is expected, what does that typically mean? That means you’re expected to have multiple publications when you come up for tenure and that you attempt to gain external funding. That’s serious research at a teaching campus. A scientist with one paper per year in a decent journal is considered to be mighty good at most teaching campuses. At very well-funded private teaching campuses, there may be higher expectations that go along with a lower teaching load. If the base teaching load is three courses per semester or higher, than it’s unlikely that “serious research” means anything more than a few pubs (though you’re usually expected to try to get a grant). If you think this is an unacceptably low level of productivity (in others), or if you think this is a sign of mediocrity, then you don’t belong on a teaching campus, because you’d get resentful of your colleagues right away.

You’ve got to be able to distinguish productivity from quality, and the fact that someone can be a great researcher while producing at a level that would be unacceptable at an R1.

What’s the best indicator of genuine campus research expectations? Look at the people who are recently tenured. What do their CVs look like? That’s what expected, or maybe just one little notch higher.

Regardless of what a campus regards as “research,” every 4-year teaching institution expects serious research at some level. All individuals are expected, at least pre-tenure, to sincerely pursue a scholarly agenda and publish. The research expectation for tenure might be just a single publication before coming up for tenure, as it was at my last job. The expectation might be 2-3 pubs before tenure (in my current position). That level of productivity might be laughable from the perspective of a research institution, but it’s serious business on a teaching campus. And when you’re teaching 3-4 full courses per semester, and your job counts on having these courses go very well, it ain’t easy.

Let’s say that you want to pursue a serious research agenda at a teaching institution, and by “serious research agenda” you mean that you want to maintain a level of research activity that will keep your CV looking like it could belong at an R1 institution. You want to publish a few papers per year and you want your research to be recognized, cited and make a difference in your discipline. Is this possible? Yes. It is common? No. Is it possible at the job to which you’re applying? Maybe. Let’s consider how you can tell if it’s possible.

If there are genuinely well-recognized researchers in the department to which you’re applying, then that’s great news for you. It’s possible! You could be one of them! That’s a wonderful sign. The flowchart ends with “sounds like a good place for research.”

What about the other branch of the flowchart? What if a department doesn’t have any productive researchers? Could a new hire run a productive research lab in a department that doesn’t have strong researchers? On some campuses this is a definite possibility; on others, it won’t be possible. How can you distinguish the former from the latter? That’s really tricky. It depends on what you think you need to run a successful research program, and if it can fit into the bounds of the institution.

A successful research lab on a teaching campus doesn’t look much like a successful lab at a research institution. Success in research at a teaching campus needs to take an entirely different route than the one that you saw in grad school. (This is a whole other long post, but here’s a start on the concept.) The things that typically result in success at research institutions just can’t happen at teaching campuses. You probably won’t have other labs with which you can collaborate. You won’t have a serious research student for more than two years, tops. You aren’t going to have postdocs to do analyses and writing on your projects, and you won’t have any single person working consistently in the lab during normal working hours. Maintaining an atmosphere of an active R1 research lab would be a full-time job, if it were even possible. You’ve got to develop a research lab that works for you on your own terms, and you need to find a campus that provides you what you need, which is individualized to what makes your program work.

What do I need to keep my lab running? Once in a while I am contacted by a colleague who wants to “visit my lab” and spend time with my “research group.” I can’t help but chuckle to myself. My lab is more often empty than not, filled with a bunch of samples that need work. When it’s not empty, it’s full of undergraduates who are more focused on studying for their exams than they are on bringing their research towards publication. (Don’t get me wrong: my research lab is critically important space, and I need more of it; I just use this space differently than R1 labs.) My students are my highest priority, but the route that I take is to emphasize productivity of my research program over providing detailed and careful mentorship to my students year-round. This might be heresy in some teaching institutions, but I think it’s a strategy that serves the interests of my students the best.

With my lab looking like I just described, how do I get research done? During the school year, aside from a few small contributions by students apart from coursework, work gets done by me. (I can’t pay students to work in my lab, and all of my students need to earn money, so they typically have outside employment.) I got to spend a few weeks at the scope this spring, to finish up a very cool project, but I’d say that 98% of my work is me sitting at a computer.

During the academic year, I’m analyzing data, writing manuscripts, and feeding collaborations in a variety of ways. This work is sometimes done best when I’m not at work. I can do some things well from home, but I might work elsewhere away from campus or home too.

The source of my data arrives in the summertime. I go to the rainforest for a few weeks, and I have 1-4 students working there for the full summer. This work, along with other small projects here and there, generates more samples and data than I can handle throughout the academic year. My manuscript backlog is substantial, and if science is going to happen predictably, year after year after year, then nearly all of that work has to be done by me.

I sometimes feel like the early British explorers of the Antarctic who brought sledge dogs but didn’t know how to use them, so they ended up pulling the sleds themselves while the dogs were running alongside without any burden. This isn’t the way things really are, and my students do make great contributions, but most of them are not yet equipped to write manuscripts and if I equipped them, then far too few of them would ever come to press.

In sum, what is it that I need from my institution to get research done? I have the freedom and flexibility to be able to focus on writing. The campus culture doesn’t expect me to provide a sophisticated mentoring agenda for every student who comes into my lab, and I’m evaluated on the basis of the output (of students) rather than the methods. The bottom line is that I am sending students on to graduate programs, and that I am producing scholarship with student authors, and that I am bringing in grant money. That is valued, and I’ve never gotten any guff about how I go about making that happen. That’s how my campus allows me to get research done.

How can you apply my personal anecdote about my own campus to figuring out how other campuses can support your productive research program? You need to identify what route – or routes – exist for research success and whether those campuses enable those routes. You need to be able to envision exactly what you need to be successful, and then see if that is possible within the job. This takes an understanding of how you run your lab and how you get stuff done. This might be hard to figure out if you haven’t done it yet.

What is the worst indicator of whether a campus can support your research? Whatever they tell you. Having administrators that support research in concept is important. However, whatever they imagine you need, and whatever they want to provide, may not be what you truly need. You have to figure out what you really need, and I think this is highly individualized.

For example, if your program relies on the upkeep of some model organism, are there technical staff or work-study funds to pay someone to maintain your critters without you having to worry about it? If your work relies heavily on some fancy piece of equipment, is there one of those on campus with a service contract that you can use for your research? If you need to go two weeks at a time to a collaborating lab during the academic year, do you have the flexibility to schedule your classes to make that happen? If you need relatively untrained students to do repetitive work in the lab, are there funds or people that enable that to happen predictably or consistently? If you have a field site six hours away for your project, does the institution have a van that you can use to take students out and do you have a student population that can afford to go away for extended work?

A lot of professors who aren’t getting much research done at teaching institutions are frustrated because their institutions lack what they need to get done – not just in terms of equipment, but in terms of flexibility. There is often a structural mismatch between a professor’s research ambitions and what is possible on campus. You want to avoid that mismatch. You can do that by being flexible in what research questions you ask and how you go about answering them, or you could do that by finding the campus that can give you what you need. (I’ve done both; the latter was more by accident than design.)

Is the way the job is structured able to give you the time and resources that enable you to focus on research? You can’t tell that from a job ad. Unless you know exactly what your research program is going to look like, you can’t even learn that from an interview.

Last, can you be a serious and well-recognized researcher on a campus that doesn’t even seem to care about research at all? You definitely can. It doesn’t matter what people say or think on your campus. If your campus gives you the time, space and resources to make you successful – whatever it is that you need – it doesn’t matter what the campus culture is at all. Talking about research on a teaching campus won’t do your research program any good. You just need to, privately and in an individualized fashion, get it done.

The hardest part about being a researcher at a teaching campus is that this part of your job is very solitary. To be successful on a teaching campus typically means that you’re doing it on your own, and with collaborators who aren’t with you on your campus. This is radically different from what you experienced at research institutions in which you worked with a lab group. Even if your buddies worked on different projects, you still had one another.

How do you run a productive research program over your career in which you won’t have a peer, or highly trained mentee, working on directly related questions? Your specific answer to this question can tell you whether or not a job at a teaching institution can support your research agenda.

How can you tell this from the institution’s website and from the job ad? You can’t. Which is why you need to apply, and then find out if you land an interview. To be continued.