Achieving a Professorship with Proper Academic Merit: Discouraging Questionable Publishing
Bibliographic record
Abstract
There are frequent discussions in many research communities about publishing in predatory and questionable outlets. It is necessary to address the researchers who publish in these publications, since this problem could be resolved if researchers stopped engaging with them. One of the factors contributing to an author’s decision to engage with these journals is the advantage of having more publications and editorial board involvement when they apply for a faculty position or a promotion. Fast-tracking promotions using questionable publications is an increasing problem, as scholars see the strategy working well for their colleagues. Universities are increasingly being called upon to take action. Promotion guidelines are vital for setting expectations, and more specifically pressures and incentives, when addressing the issue of questionable journals. In the case study presented in this article, new promotion guidelines have been developed at Kwame Nkrumah University of Science and Technology in Ghana to discourage faculty members from publishing in questionable journals. A verification process for all publications listed in promotion applications has been implemented. Since the implementation of this scheme in October 2019, 221 researchers have applied for promotion. Our analysis shows that one fifth of submitted publications do not meet the new criteria. Furthermore, we find no correlation between the proportion of verified publications and an applicant’s college or total number of listed publications. The implications of these findings are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.088 | 0.377 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".