A Kuhnian gap between research publishing and academic success

A Kuhnian gap between research publishing and academic success
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There is a gap in research publishing and how it relates to academic success. On the one hand, there are scientists complaining of low funds, being short-staffed, low-quality or absent equipment, disoptimal employment/tenure terms, bureaucratic incompetence and political interference. On the other, there are scientists who describe their success within academia in terms of being published in XYZ journals (with impact factors of PQR), having high h-indices, having so many papers to their names, etc.

These two scenarios – both very real in India and I imagine in most countries – don’t straightforwardly lead to the other. They require a bridge, a systemic symptom that makes both of them possible even when they’re incompatible with each other. This bridge is those scientists’ attitudes about what it’s okay to do in order to keep the two façades in harmonious coexistence.

What is it okay to do? For starters, keep the research-publishing machinery running in a way that allows them to evaluate other scientists on matters other than their scientific work. This way, lack of resources for research can be decoupled from scientists’ output in journals. Clever, right?

According to a study published a month ago, manuscripts that include a Nobel laureate’s name among the coauthors are six-times more likely to be accepted for publication than those without a laureate’s name in the mast. This finding piles on other gender-related problems with peer-review, including women’s papers being accepted less often as well as men dominating the community of peer-reviewers. Nature News reported:

Knowledge of the high status of a paper’s author might justifiably influence a reviewer’s opinion: it could boost their willingness to accept a counterintuitive result, for example, on the basis of the author’s track record of rigour. But Palan’s study found that reviewers’ opinions changed across all six of the measures they were asked about, including the subject’s worthiness, the novelty of the information and whether the conclusions were supported. These things should not all be affected by knowledge of authorship, [Palan, one of the paper’s coauthors, said].

Palan also said the solution to this problem is for journals to adopt double-anonymised peer-review: the authors don’t who the reviewers and the reviewers don’t know who the authors are. The most common form of peer-review is the single-blind variety, where the reviewers know who the authors are but the authors don’t know who the reviewers are. FWIW, I prefer double-anonymised peer-review plus the journal publishing the peer-reviewers’ anonymised reports along with the paper.

Then again, modifying peer-review would still be localised to journals that are willing to adopt newer mechanisms, and thus be a stop-gap solution that doesn’t address the use of faulty peer-review mechanisms both inside journals and in academic settings. For example, given the resource-mininal context in which many Indian research institutes and universities function, hiring and promotion committees often decide whom to hire or promote based on which journals their papers have been published in and/or the number of times those papers have been cited.

Instead, what we need is systemic change that responds to all the problems with peer-review, instead of one problem at a time in piecemeal fashion, by improving transparency, resources and incentives. Specifically: a) make peer-review more transparent, b) give scientists the resources – including time and freedom – to evaluate each others’ work on factors localised to the context of their research (including the quality of their work and the challenges in their way), and c) incentivise scientists to do so in order to accelerate change and ensure compliance.

The scientometric numbers, originally invented to facilitate the large-scale computational analysis of the scientific literature, have come to subsume the purpose of the scientific enterprise itself: that is, scientists often want to have good numbers instead of want to do good science. As a result, there is often an unusual delay – akin to the magnetic hysteresis – between the resources for research being cut back and the resulting drop in productivity and quality showing in the researchers’ output. Perhaps more fittingly, it’s a Kuhnian response to paradigm change.