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Record W3010912969 · doi:10.1093/jpo/joaa006

Inside the ‘Red Circle’: the production of China’s corporate legal elite

2020· article· en· W3010912969 on OpenAlexaff
Jingqi Zhu, Yang Zhao, Sida Liu

Bibliographic record

VenueJournal of Professions and Organization · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEliteProduction (economics)ChinaGlobalizationBeijingPolitical scienceWork (physics)EconomicsLawPoliticsEngineering

Abstract

fetched live from OpenAlex

Abstract This article examines the production of corporate legal elite through a systematic analysis of the profiles of the first three cohorts of partners in nine elite corporate law firms in Beijing. We argue that the social production of the Chinese corporate legal elite is primarily an outcome of domestic social factors rather than international factors. It is characterized by local elite recruitment from elite universities and endogenous elite circulation within the Red Circle firms. International credentials and work experience come only secondary to education and work experience in elite Chinese law schools and law firms for achieving elite status in the profession. Yet, international experience plays a role in promoting gender equality in elite professional service firms. This article contributes to the study of globalization and elite production in professional service firms by investigating how local and global forces manifest themselves in elite production in a major emerging market.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.048
GPT teacher head0.333
Teacher spread0.286 · 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 designQualitative
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

Citations16
Published2020
Admission routes1
Has abstractyes

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