Bibliographic record
Abstract
I. Writing the Biographies of Women in LawIn her 1999 essay about the first women to become historians at the end of the 19th century, Joan Wallach Scott suggested that exploring women's access to the professions requires us to ask 'new questions': How are those who [first] cross the threshold received?If they belong to a group different from the group already 'inside,' what are the terms of their incorporation?How do the new arrivals understand their relationship to the place they have entered?What are the terms of the identity they establish?'Scott's suggestion about the need for new questions about women's access to the professions is clearly reflected in a flourishing feminist literature about how to write the biographies of women.For example, Gerda Lerner argued that women's biographies must inevitably confront gendered social realities to take account of how women 'function [ed] in [a] male-defined world on their own terms'.2 Lerner's insight may be particularly relevant to the first women lawyers, who necessarily engaged with issues of gender when they tried to gain admission to the 'gentleman's profession' of law at the end of the nineteenth century.3 As the first women to 'cross the threshold' of the legal professions, they functioned in the 'gentleman's profession' of law by adopting a number of different strategies, depending on how they were accepted by those already on 'the inside,' how they understood their roles as lawyers, and how they established identities as 'women' who were also 'legal professionals'.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.062 |
| Scholarly communication | 0.015 | 0.033 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 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".