When "Feminist Beliefs" Became Credible as "Political Opinions": Returning to a Key Moment in Canadian Refugee Law
Bibliographic record
Abstract
This submission highlights the key feminist inroad into the protection of refugee women fleeing persecution over the past twenty years—the moment in which Canadian state machinery formally engaged the fact that women may suffer social, political, or other forms of persecution because they are women. Drawing upon a myriad of sources, the submission illustrates the highly personal experiences that colour this engagement between feminism and the law, as well as the challenge of making women's experiences as women legally relevant and politically meaningful. Le présent exposé met en lumière les incursions féministes clés dans la protection des réfugiées qui ont fui la persécution durant les vingt dernières´années—le moment pendant lequel l'État canadien a pris formellement connaissance du fait que les femmes peuvent subir des formes particulières de persécution sociale, politique ou autre, parce qu'elles sont femmes. En se fondant sur de multiples sources, l'exposé illustre les expériences très personnelles qui colorent cet engagement entre le féminisme et le droit aussi bien que le défi de rendre les expériences des femmes comme femmes pertinentes en droit et significatives sur le plan politique.
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 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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.067 | 0.057 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".