Gender Equality I.,.,!Sa* = f l q J $ *." " &,j &;"<$$p " "!:A"-
Bibliographic record
Abstract
Pour montrer I'Pvolution des droits a I'PgalitP, lkuteure utilise, h commencer par les '5fameuses"du cas Personne en 1929, la tradition chez les Canadiennes, de se seruir du systi.me judiciaire incluant les tribunaux administratifj. When I graduated from law school in the late '50s, I was one of only six women in a class of 200. I was asked at the time, on more than one occasion, whether I was studying law because I wanted to marry a lawyer, a question that would be inconceivable today. The first time I appeared in court on behalf of a client, I was asked by a colleague whether I would cry if I lost the case. And I remember one occasion in the Montreal courthouse when three male colleagues stood guard while I used the men's washroom, because it had not occurred to anyone to put aladies ' room in the area reserved for lawyers.
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 | 0.024 | 0.020 |
| Meta-epidemiology (narrow) | 0.010 | 0.009 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.013 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.038 | 0.100 |
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".