Gender Differences in Publication among University Professors in Canada*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cet article analyse un important sondage à l'échelle canadienne et aborde la probématique de la productivité: pourquoi les professeures d'université publient‐elles moins que leurs collégues hommes ? Les résultats montrent que, dans l'ensemble, les femmes ont publié moins que les hommes — et ce, de manière significative —, à la fois durant leur carrière et au cours des trois années qui ont précédé le sondage. Cependant, des analyses multivariables révèlent que des différences s'avèrent plus prononcées dans les données touchant la carrière que dans celles de la courte période. La plus grande différence entre les hommes et les femmes a trait au fait de publier dans une revue à comité de lecture ou sans, et s'applique à toute leur carrière. Enfin, des différences se laissent expliquer par des différences de rang, d'années depuis l'obtention du doctorat, la discipline, le type d'université ainsi que le temps consacré a la recherche. Des problèmes d'évaluation des prédicteurs de la productivité en recherche sont discutés. This paper analyses a large Canadian national survey of professors and tackles the “productivity puzzle” as to why female scientists publish less than male scientists. Results show that, in aggregate, Canadian female professors have published significantly less than their male counterparts, both over their lifetimes and during the three years before the survey. However, multivariate analyses reveal that gender differences in publication are more pronounced in the lifetime data than in the data for the shorter period. Much of the difference in publication between men and women of the academy is in refereed and non‐refereed articles and reports over their career. Finally, gender differences in publication are largely accounted for by differences in rank, years since PhD, discipline, type of university and time set aside for research. Problems of assessing predictors of research productivity are discussed.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.015 | 0.036 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it