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Record W3122460979 · doi:10.1139/facets-2020-0044

Academic criteria for promotion and tenure in faculties of medicine: a cross-sectional study of the Canadian U15 universities

2021· article· en· W3122460979 on OpenAlexaffvenueabout
Danielle B. Rice, Hana Raffoul, John P. A. Ioannidis, David Moher

Bibliographic record

VenueFACETS · 2021
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsUniversity of WaterlooMcGill UniversityOttawa Hospital
Fundersnot available
KeywordsPromotion (chess)ReputationIncentiveGuidelineMedical educationPublishingPsychologyPublic relationsMedicinePolitical scienceEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.210
GPT teacher head0.487
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2021
Admission routes3
Has abstractyes

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