Gender and Professionalism in Law: The Challenge of (Women’s) Biography
Bibliographic record
Abstract
This paper explores the story of a woman who “created” her life in the law in the late nineteenth and early twentieth centuries. Although now almost unknown, Cornelia Sorabji achieved prominence as a woman pioneer in the legal profession, who provided legal services to women clients in northern India, the Purdahnashins. Sorabji’s experiences as a woman in law were often similar to the stories of other first women lawyers in a number of different jurisdictions at the end of the nineteenth century: all of these women had to overcome gender barriers to gain admission to the legal professions, and they were often the only woman in law in their jurisdictions for many years. Yet, as Sorabji’s story reveals, while ideas about gender and the culture of legal professionalism could present formidable barriers for aspiring women lawyers, these ideas sometimes intersected in paradoxical ways to offer new opportunities for women to become legal professionals. In exploring the impact of gender and legal professionalism on Sorabji’s legal work, the paper also suggests that her story presents a number of challenges and contradictions that may require new approaches to gender history so as to capture the complexity of stories about women lawyers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.041 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".