Bibliographic record
Abstract
When Clara Brett Martin decided in the late nineteenth century that she wished to become a lawyer, this aspiration was considered unusual enough that it required a statutory amendment by the Ontario legislature to allow her access to legal education and admission to the bar.In 1897, she became the first woman admitted to the bar in Canada, and her example was followed over the next decades by women in other provinces.The number of women in law schools and in the legal profession remained small, however.It was not until the 1970s that women began to make up a significant proportion of law students.Since the 1980s, the numbers of male and female law students in Canadian law schools have been more or less equal. 1 As one might expect, this shift eventually affected the composition of the legal profession as well.According to statistics published by the non-profit organization Catalyst, based on 2018 data, 1It is only in the last few years that there has been any consideration of gender identity in examining law school demographics, and there is little current information about how this might affect future discussion of this issue.We understand that gender identity is a spectrum, and that terms and pronouns used throughout the book may not resonate with everyone.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".