Bibliographic record
Abstract
Women now make up at least 50 percent of students in the entry classes in most Canadian law schools. In some law schools women constitute about 50 percent of the tenured or tenure stream professoriate – a marked change from the early 1990s. This feminization of legal education is not the result of affirmative action but rather the increased acceptability of a legal education for women, the removal of formal and informal barriers to women's admission to law school, and women's strong academic records. Despite the fact that legal academia is now populated by virtually equal numbers of women and men, and despite the greater visibility of women (including feminist women) in positions of power, there remains a critical need for feminist presence, analysis, and critique in the 21st century. Feminism is also important because women continue to encounter barriers in gaining access to justice for reasons related to gender, in addition to other factors such as poverty. This essay draws partly on the author's own experience as a law student and law professor over the past 35 years, as well as the experience of students with whom she has engaged over that period. Her argument is that although many important changes have taken place in legal academia, and spaces made for individuals who were previously excluded, serious challenges remain, making it essential that a feminist presence and voice and a commitment to social justice be central. The author ends by briefly considering whether and how that feminist voice is changing in the 21st century.
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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.036 | 0.117 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".