Gender disparity trends in the Canadian legal profession: a case study of Nova Scotia lawyers
Bibliographic record
Abstract
The objective of this thesis is to develop a better understanding of the gender disparity in Canada's legal profession. Focusing on the province of Nova Scotia, I examine why this province does not follow the trend of the rest of the country in terms of women being under-represented in law. Both qualitative and quantitative data are employed to answer this important question. The qualitative data was collected from four semi-structured interviews with women lawyers currently practicing in Nova Scotia, while quantitative data was collected from Canadian legal associations and the governments. For comparative analysis, countries in which gender quotas are implemented in the legal profession are also reviewed. This provides insight regarding the potential advantages of quota initiatives, as well as providing direction for policy suggestions for Canada's legal profession. Three hypotheses were posed. Firstly, that despite the trend in Nova Scotia of women outnumbering men in law firms, men will nonetheless hold a larger proportion of executive or partner positions in those firms, which follows with the rest of Canada. Secondly that there is a correlation between the greater proportion of small law firms in Nova Scotia and the number of women who are practicing law in the province. Thirdly, because gender representation in Nova Scotia's legal field is an exception to the overall trend in Canada, it is anticipated that it has a quota-like initiative in place to ensure a more equal representation of women in the legal profession. The first hypothesis was proven through qualitative and quantitative data collection as was the second hypothesis. The third hypothesis was inconclusive, however data from some European countries provide insight into possible policy options for Canada.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.026 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".