Gender and the Practice of Law in Canada
Bibliographic record
Abstract
One may ask why paralegals are shown to be the only profession in the criminal justice system that has an overrepresentation of women employees, while male legal professionals tend to be identified as attorneys – perhaps it is due to societal ideologies of male dominance. Male dominated industries and occupations, like law practice, have fewer women employees, women have a harder time excelling in their field, and are less likely to attain partnership promotion. Career expansion is difficult on women in the male-dominated field of law. Although women are completing law school at the same rate as men, higher numbers of women are leaving law (attrition) compared to men, losing their talent from the workforce, and contributing to continuing sexist attitudes. This study therefore aims to investigate the reasons why females are less likely to remain working as attorneys compared to males, despite there being approximately equal representation of males and females in law schools. This loss of women is not only important for tackling discrimination, which is an important goal for any industry, but also for reducing the loss of highly trained intelligent professionals in the law firms simply due to their gender, requiring further training of new staff. Furthermore, this research will not only confirm the high attrition of women, but will also inform the law firms on the reasons behind it, allowing counteractive measures to be developed that might increase retention of their female staff.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".