Thursday, June 15, 2017

N-best evaluation for hiring and promotion

How can we create incentive-compatible evaluation of scholarship? Here's a simple proposal, discussed around a year ago by Sanjay Srivastava and floated in a number of forms before that (e.g., here):
The N-Best Rule: Hiring and promotion committees should solicit a small number (N) of research products and read them carefully as their primary metric of evaluation for research outputs. 
I'm far from the first person to propose this rule, but I want to consider some implementational details and benefits that I haven't heard discussed previously. (And just to be clear, this is me describing an idea I think has promise – I'm not talking on behalf of anyone or any institution).

Why do we need a new policy for hiring and promotion? How do two conference papers on neural networks for language understanding compare with five experimental papers exploring bias in school settings or three infant studies on object categorization? Hiring and promotion in academic settings is an incredibly tricky business. (I'm focusing here on evaluation of research, rather than teaching, service, or other aspects of candidates' profiles.) How do we identify successful or potentially successful academics, given the vast differences in research focus and research production between individuals and areas? Two different records of scholarship simply aren't comparable in any sort of direct, objective manner. The value of any individual piece of work is inherently subjective, and the problem of subjective evaluation is only compounded when an entire record is being compared.

To address this issue, hiring and promotion committees typically turn to heuristics like publication or citation numbers, or journal prestige. These heuristics are widely recognized to promote perverse incentives. The most common, counting publications, leads to an incentive to do low-risk research and "salami slice" data (publish as many small papers on a dataset as you can, rather than combining work to make a more definitive contribution). Counting citations or H indices is not much better – these numbers are incomparable across fields, and they lead to incentives for self-citation and predatory citation practices (e.g., requesting citation in reviews). Assessing impact via journal ranks is at best a noisy heuristic and rewards repeated submissions to "glam" outlets. Because they do not encourage quality science, these perverse incentives have been implicated as a major factor in the ongoing replicability/reproducibility issues that are facing psychology and other fields.

Thursday, June 1, 2017

Confessions of an Associate Editor

For the last year and a half I've been an Associate Editor at the journal Cognition. I joined up because Cognition is the journal closest to my core interests; I've published nine papers there, more than in any other outlet by a long shot. Cognition has been important historically, and continues to publish recent influential papers as well. I was also excited about a new initiative by Steve Sloman (the EIC) to require authors to post raw data. Finally, I joined knowing that Cognition is currently an Elsevier journal. I – perhaps naively – hoped that like Glossa, Cognition could leave Elsevier (which has a very bad reputation, to say the least) and go open access. I'm stepping down as an AE in the fall because of family constraints and other commitments, and so I wanted to take the opportunity to reflect on the experience and some lessons I've learned.

Be kind to your local editor. Editing is hard work done by ordinary academics, and it's work they do over and above all the typical commitments of non-editor academics. I was (and am) slow as an editor, and I feel very guilty about it. The guilt over not moving faster has been the hardest aspect of the job; often when I am doing some other work, I will be distracted by my slipping editorial responsibilities.1 Yet if I keep on top of them I feel that I'm neglecting my lab or my teaching. As a result, I have major empathy now for other editors – and massive respect for the faster ones. Also, whenever someone complains about slow editors on twitter, my first thought is "cut them some slack!"

Make data open (and share code too, while you're at it)! I was excited by Sloman's initiative for data openness when I first read about it. I'm still excited about it: It's the right thing to do. Data sharing is prerequisite for ensuring the reproducibility of results in papers, and enables reuse of data for folks doing meta-analysis, computational modeling, and other forms of synthetic theoretical work. It's also very useful for replication – students in my graduate class do replications of published papers and often learn a tremendous amount about the paradigm and analyses of the original experiment by looking at posted data when they are available. But sharing data is not enough. Tom Hardwicke, a postdoc in my lab and in the METRICS center at Stanford, is currently doing a study of computational reproducibility of results published in Cognition – data are still coming in, but our first impression is that it's often difficult to reproduce the findings in a good number of papers based on the raw data and their written description of analyses. Cognition and other journals can do much more to facilitate posting of analytic code.

Open access is harder than it looks. I care deeply about open access – as both an ethical priority and a personal convenience. And the journal publishing model is broken. At the same time, my experiences have convinced me that it is no small thing to switch a major journal to a truly OA model. I could spend an entire blogpost on this issue alone (and maybe I will later), but the key issue here is money: where it comes from and where it goes. Running Cognition is a costly affair in its current form. There is an EIC, two senior AEs, and nine other AEs. All receive small but not insignificant stipends. There is also a part-time editorial assistant, and an editorial software platform. I don't know most of these costs, but my guess is that replicating this system as is – without any of the legal, marketing, and other infrastructure – would be minimally $150,000 USD/year (probably closer to 200k or more, depending on software).