Academic criteria for promotion and tenure in faculties of medicine: a cross-sectional study of the Canadian U15 universities
Bibliographic record
Abstract
Background: The objective of this study was to determine the presence of a set of prespecified criteria used to assess scientists for promotion and tenure within faculties of medicine among the U15 Group of Canadian Research Universities. Methods: Each faculty guideline for assessing promotion and tenure was reviewed and the presence of five traditional (peer-reviewed publications, authorship order, journal impact factor, grant funding, and national/international reputation) and seven nontraditional (citations, data sharing, publishing in open access mediums, accommodating leaves, alternative ways for sharing research, registering research, using reporting guidelines) criteria were collected by two reviewers. Results: Among the U15 institutions, four of five traditional criteria (80.0%) were present in at least one promotion guideline, whereas only three of seven nontraditional incentives (42.9%) were present in any promotion guidelines. When assessing full professors, there were a median of three traditional criteria listed, versus one nontraditional criterion. Conclusion: This study demonstrates that faculties of medicine among the U15 Group of Canadian Research Universities base assessments for promotion and tenure on traditional criteria. Some of these metrics may reinforce problematic practices in medical research. These faculties should consider incentivizing criteria that can enhance the quality of medical research.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".