This is a bit trivial, but I was recently on travel, and I often ponder a couple of things when traveling. One is how to use my work time more efficiently. Or more specifically, what fraction of requests to say yes to, and which ones to choose? It’s a question I know a lot of other scientists ask themselves, and it’s a moving target as the number of requests change over time, for talks, reviews, etc.
The other thing is that I usually get a rare chance to sit and watch Sportscenter, and I'm continually amazed by how many statistics are now used to discuss sports. Like “so-and-so has a 56% completion percentage when rolling left on 2nd down” or “she’s won 42% of points on her second serve when playing at night on points that last less than 8 strokes, and when someone in the crowd sneezes after the 2nd stroke.” Ok, I might be exaggerating a little, but not by much.
So it gets me wondering why scientists haven’t been more pro-active in using numbers to measure our perpetual time management issues. Take reviews for journals as an example. It would seem fairly simple for journals to report how many reviews different people perform each year, even without revealing who reviewed which papers. I’m pretty sure this doesn’t exist, but could be wrong (The closest thing I’ve seen to this is Nature sends an email at the end of each year saying something like “thanks for your service to our journal family, you have reviewed 8 papers for us this year”). It would seem that comparing the number of reviews to the number of papers you get reviewed by others (also something journals could easily report) would be a good measure of whether each person is doing their part.
Or more likely you’d want to share the load with your co-authors, but also account for the fact that a single paper usually requires about 3 reviewers. So we can make a simple “science citizen index” or “scindex” that would be
SCINDEX = A / (B x C/D) = A x D / (B x C)
A = # of reviews performed
B = # of your submissions that get reviewed (even if the paper ends up rejected)
C = average number of reviews needed per submission (assume = 3)
D = average number of authors per your submitted papers
Note that to keep it simple, none of this counts time spent as an editor of a journal. And it doesn’t adjust for being junior or senior, even though you could argue junior people should do less reviews and make up for it when they are senior. And I’m sure some would complain that measuring this will incentivize people agreeing but then doing lousy reviews. (Of course that never happens now). Anyhow, if this number is equal to 1 than you are pulling your own weight. If it’s more than 1 you are probably not rejecting enough requests. So now I’m curious how I stack up. Luckily I have a folder where I save all reviews and can look at the number saved in a given year. Let’s take 2013. Apparently I wrote 27 reviews, not counting proposal or assessment related reviews. And Google Scholar can quickly tell me how many papers I was an author on in that year (14), and I can calculate the average number of authors per paper (4.2). Let’s also assume that a few of those were first rejected after review elsewhere (I don’t remember precisely, but that’s an educated guess), so that my total submissions were 17. So that makes my scindex 27 x 4.2 / (17 x 3) = 2.2. For 2012 it was 3.3.
Holy cow! I’m doing 2-3 times as many reviews as I should be reasonably expected to. And here I was thinking I had a reasonably good balance of accepting/rejecting requests. It also means that there must be lots of people out there who are not pulling their weight (I’m looking at you, Sol).
It would be nice if the standard citation reports add something like a scindex to the h-index and other standards. Not because I expect scientists to be rewarded for being a good citizen, though that would be nice, or because it would expose the moochers. But because it would help us make more rational decisions about how much of the thankless tasks to take on. Or maybe my logic is completely off here. If so, let me know. I’ll blame it on being tired from too much travel and doing too many reviews!
So David, I'm willing to accept the logic for your index. What I'm wondering is whether a more senior scientist should actually aim to have a higher 'scindex' to make up for earlier years where reviewing was not as much a part of his/her expected activities. You specifically mention this in the 5th paragraph - and this assertion seems defendable.ReplyDelete
This still leaves the overall question though of how far one should go in this 'repayment' (if we wish to consider this an academic debt). And I would further argue that even if one has personally accounted for their costs to the system from junior efforts and dutifully repaid the debt there may still be reason to expect more senior scientist to maintain a 'scindex' above 1 because other metrics such as the h-index tend to increase with time even in the face of reduced output. But aside from this particular argument I do think you should be able to feel as though you've done your share at the levels you've described here.
Now if I might - what would you think of a different proposal. Let's name reviewers and credit (or discredit) them in some fashion. Several journals are already listing some reviewer/editors. But I'm wondering if there have been any attempts to quantitate (ala impact metrics for one example) review quality. This seems fraught with potential problems on the surface, but I have faith that if intelligent folk kick the notion around a bit there may be a way to get at review quality.
Just goes to show how even non-nerd sports types can be motivated to use math when winning is on the line. First baseball with "sabermetrics" and now practically every winning pro sports team has become data-focused.ReplyDelete
In the academic world, "winning" means publishing high-impact papers in high-impact journals. What an editor really needs to know is which reviewers have the most effect on the creation of high-impact papers. This means getting out of the old-boy network method of review assignment and creating a new method of "papermetrics" that collects everything from turnaround time on reviews to number of words of comments to emotional content of word choice, then correlating these with post-publication citation and impact profiles. At the very least, it's yet another publication for your vita; at best, it would make a permanent improvement in the efficiency of the publication process.