Bibliographic record
Abstract
Au Canada et dans d’autres pays occidentaux, la ventilation par sexe dans l’enseignement superieur est passee d’un desequilibre en faveur des hommes a un desequilibre en faveur des femmes. Les programmes visant a attirer les femmes vers l’enseignement superieur ont ete tres efficaces. C’est ainsi que l’Universite de Guelph compte 70 % d’etudiantes. Les enseignants doivent-ils etre preoccupes par ce phenomene? Ce desequilibre peut-il avoir des effets negatifs a court et a long terme? Si tel est le cas, que peuvent faire les educateurs pour y remedier? Faut-il mettre en place des programmes visant a attirer les hommes vers l’enseignement superieur? Quelles sont les mesures qui peuvent etre prises sur le plan de l’accessibilite pour arriver a un equilibre hommes/femmes dans l’enseignement superieur? Cet article examine les changements intervenus dans la repartition hommes/ femmes dans les universites et colleges au Canada, aux Etats-Unis et dans d’autres pays. Il expose les causes et les effets eventuels sur le plan economique, social et politique, du desequilibre entre hommes et femmes. On etudie les techniques d’accessibilite qu’il serait possible d’utiliser pour aboutir a un equilibre hommes/femmes dans les programmes des universites et des colleges.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".