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Record W3118765194 · doi:10.4324/9780203831427-9

(De-)sexing the woman lawyer: Rosemary Hunter

2011· article· en· W3118765194 on OpenAlexaboutno aff
Rosemary Hunter

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsCommonwealthLegal professionDominance (genetics)White (mutation)LawMiddle classPolitical scienceGender studiesSociology

Abstract

fetched live from OpenAlex

A series of studies of women in the legal profession in the late 1990s, in the UK and Anglo-Commonwealth jurisdictions such as Australia and Canada, revealed that, although women had been entering the profession in significant numbers for over fifteen years, they were still regarded very much as professional ‘outsiders’.1 Legal practice remained not only numerically but also culturally maledominated. Subsequent studies of professional socialisation and the self-images projected by the solicitors’ and barristers’ branches of the profession in recruitment materials indicate that, ten years on, and despite rapidly declining numerical dominance, the legal field continues to be characterised by the cultural domination of white, middle-class men, and the marginalisation of women and other non-traditional entrants.2

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0090.008
Scholarly communication0.0030.005
Open science0.0010.003
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.112
GPT teacher head0.381
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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