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Record W3121416037 · doi:10.1017/s0022109017000059

Gender and Board Activeness: The Role of a Critical Mass

2017· article· en· W3121416037 on OpenAlexaff
Miriam Schwartz-Ziv

Bibliographic record

VenueJournal of Financial and Quantitative Analysis · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsUniversity of Toronto
FundersHebrew University of Jerusalem
KeywordsCritical mass (sociodynamics)Gender equityAttendanceGlass ceilingBusinessRepresentation (politics)AccountingPhenomenonEquity (law)Government (linguistics)Gender diversityDemographic economicsPublic relationsPolitical scienceCorporate governanceEconomicsFinanceLaw

Abstract

fetched live from OpenAlex

This study analyzes detailed minutes of board meetings of business companies in which the Israeli government holds a substantial equity interest. Boards with at least 3 directors of each gender are found to be at least 79% more active at board meetings than those without such representation. This phenomenon is driven by women directors in particular; they are more active when a critical mass of at least 3 women is in attendance. Gender-balanced boards are also more likely to replace underperforming chief executive officers (CEOs) and are particularly active during periods when CEOs are being replaced.

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)0.010
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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.113
GPT teacher head0.370
Teacher spread0.257 · 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

Citations294
Published2017
Admission routes1
Has abstractyes

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