Does Counting Publications Provide Any Useful Information about Academic Performance
Bibliographic record
Abstract
At the heart of the Publish or Perish Syndrome is the practice of valuing quantity of publication over quality. This practice is so pervasive that it extends even to the selection process for positions in which one would not expect publica tion counts to be a significant indicator of a person's qualifications to do the job.1 This was brought home to me several years ago when I served on a selection committee for an administrator position. The committee agreed early on that academic should be one of several criteria which the successful candidate ought to meet. Members of the committee then looked at the Curriculum Vitae of potential candidates and screened for credibility simply and solely on the basis of the numbers of the candidates' publications. In addition, the selection committee received input from faculty-at-large which ■■iurged them to reject any candidates whose Curriculum Michael Skolnik is a Vitae did not show a substantial list of publications. professor of higher There was no suggestion at all that quality or impact of education at the Ontario candidates' publications should be a criterion, and no Institute for Studies in discussion in the committee of the content or signifi Education of the cance of candidates' publications. University of Toronto, The practice of judging a professor's worth by Toronto, Ontario, counting his or her publications is common through Canada. out the academy, whether the professor's primary IS
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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 | 0.059 | 0.385 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.026 | 0.050 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.033 |
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".