Bibliographic record
Abstract
Tout comme la France, le Québec a connu d’importants débats sur la laïcité au cours des dernières années. Dans ce contexte, une certaine lecture de l’histoire des femmes et le principe d’égalité entre les sexes ont été utilisés comme arguments pour justifier la mise en place de politiques ciblant particulièrement certains signes religieux, dont le hijab. Or, est-il exact d’affirmer que, historiquement, c’est essentiellement « la religion » qui aurait causé l’oppression des femmes ? Est-ce juste de présumer que, conséquemment, la laïcité garantirait une amélioration de leur condition ? En m’appuyant sur l’histoire des femmes à plusieurs moments de « modernisation » et de « laïcisation » au Québec, je propose de remettre en question ce postulat central aux discours en faveur d’une laïcité républicaine.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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; both teacher heads agree on what is shown here.
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".