The Impact of Feminism on the Research and Writing of Medical History: A\n Personal View
Bibliographic record
Abstract
Using my own research on the Canadian medical profession and its treatment of women in the first half of the twentieth century, I illustrate four ways in which feminist theory and action has influenced historical scholarship: first, the recognition of women as a socially constructed gender; second, concerns about the increasing medical intervention in women's bodies; third, the recent sensitivity with regards to women's agency; and fourth, the feminist critique of science itself.In turn, I argue that historical research can help nuance our view of the present and where we want to go in the future.RESUME En me servant de ma propre recherche sur la profession medicale au Canada et sur la maniere dont elle a traite les femmes dans la premiere moitie du vingtieme siecle, j'illustre quatre facons par lesquelles la theorie et Paction feministe ont influence l'erudite historique : premierement la reconnaissance que les femmes sont un sexe qui est construit socialement, deuxiemement, les inquietudes au sujet de la hausse des interventions medicales sur le corps des femmes; troisiemement, la sensibilite a l'egard de I'agence feminine; et quatriemement, la critique de la science elle meme.A partir de cela, je denote que la recherche historique peut aider a nuancer notre vue du present et ou nous voulons nous diriger dans l'avenir.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.069 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".