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Record W638823170 · doi:10.1163/17087384-12342059

Current Demographics in Large Corporate Law Firms in South Africa

2015· article· en· W638823170 on OpenAlexvenueno aff
Jonathan Klaaren

Bibliographic record

VenueAfrican Journal of Legal Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsDemographicsCorporate lawPolitical scienceLegal professionLawRace (biology)AccountingBusinessSociologyCorporate governanceGender studiesDemographyFinance

Abstract

fetched live from OpenAlex

By contrast with the judges and the advocates, the issue of race and gender representivity in the attorneys segment of the legal profession generally and in large corporate law firms specifically has not received significant attention, in part due to the lack of accurate statistics and a thin research tradition. Addressing the gap, a 2013 survey investigated the demographics of legal professionals in large corporate law firms in South Africa. The chief finding of the survey is that South Africa’s major corporate law firms are still dominated by white men, especially in their upper echelons. Further, nearly half of the African women professionally employed in large corporate law firms (48.1%) are candidate attorneys, which is to say non-admitted legal professionals. These findings are consistent with the few earlier studies that have been conducted and indicate the need for further detailed research into the social dynamics of the African legal profession.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.204
GPT teacher head0.409
Teacher spread0.205 · 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 designObservational
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

Citations8
Published2015
Admission routes1
Has abstractyes

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