Combien ou comment ? Les femmes canadiennes dans les récits scolaires et dans la mémoire collective, rétrospective des recherches depuis 1980
Bibliographic record
Abstract
L’histoire des femmes peine toujours à faire sa place dans la mémoire collective et dans l’enseignement de l’histoire au Canada. Le présent article propose une courte recension des écrits publiés depuis 1980 à ce sujet. Si certaines recherches ont posé la question à savoir « combien » de femmes étaient présentes dans les récits, c’est surtout le « comment » qui a intéressé les chercheuses et les chercheurs. Les études, provenant des départements d’histoire et des facultés d’éducation, ont porté sur trois aspects : la mémoire collective, les manuels scolaires et la compréhension des élèves du rôle des femmes dans l’histoire. Malgré la très lente évolution de la représentation des femmes dans la trame narrative de l’histoire canadienne, l’analyse des recherches sur cette question permet un regard critique sur la construction des récits historiques et mène à une réflexion nécessaire sur les événements commémoratifs récents. Women’s history is still waiting to gain more place in collective memory and history teaching in Canada. This article provides a brief review of the literature published since 1980 on this topic. While some research has posed the question of "how many" women are present in the narratives, "how they are represented" was the focus of most researchers. Studies from history departments and faculties of education concentrated on three aspects: collective memory, textbooks, and student understanding of women’s agency. Despite the very slow evolution of the representation of women in the narrative framework of Canadian history, this paper allows a critical perspective at the construction of historical narratives and leads to a necessary closer look on recent commemorative events.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".