Precedents, Patterns and Puzzles: Feminist Reflections on the First Women Lawyers
Bibliographic record
Abstract
This paper initially examines the historical precedents established by some of the first women who entered the “gentleman’s profession” of law in different jurisdictions, as well as the biographical patterns that shaped some women’s ambitions to enter the legal professions. The paper then uses feminist methods and theories to interpret “puzzles that remain unsolved” about early women lawyers, focusing especially on two issues. One puzzle is the repeated claims on the part of many of these early women lawyers that they were “lawyers”, and not “women lawyers”, even as they experienced exclusionary practices and discrimination on the part of male lawyers and judges—a puzzle that suggests how professional culture required women lawyers to conform to existing patterns in order to succeed. A second puzzle relates to the public voices of early women lawyers, which tended to suppress disappointments, difficulties and discriminatory practices. In this context, feminist theories suggest a need to be attentive to the “silences” in women’s stories, including the stories of the lives of early women lawyers. Moreover, these insights may have continuing relevance for contemporary women lawyers because it is at least arguable that, while there have been changes in women’s experiences, there has been very little transformation in their work status in relation to men.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.032 | 0.066 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".