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Record W4414026169 · doi:10.1111/corg.70004

Do Social Trust and Tolerance Affect Board Gender Diversity? An International Evidence

2025· article· en· W4414026169 on OpenAlexaff
Fatemeh Kordi, Min Maung

Bibliographic record

VenueCorporate Governance An International Review · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAffect (linguistics)Gender diversityDiversity (politics)Corporate governanceBusinessPolitical sciencePsychologyLawFinanceCommunication

Abstract

fetched live from OpenAlex

ABSTRACT Research question/Issue The paper investigates how social trust and tolerance affect board gender diversity levels in 67 countries. Research Findings/Insights We find robust evidence that countries in which people display trust toward strangers, people of other countries, and other religions have more gender‐diverse boards. We also find that tolerance toward homosexuals, immigrants, and people of other religions and different races promotes gender‐diverse boards. Theoretical/Academic Implications This paper relies on social trust, tolerance, and institutional theories. Trust and tolerance theories suggest that trusting others and displaying tolerance toward people who are different from oneself have many important social and economic consequences. We relate social trust and tolerance theories to governance and board gender diversity in a large cross‐section of countries. Practitioner/Policy Implications Board gender diversity has both practical and policy implications. Our findings suggest that legislating comprehensive gender quotas may not be sufficient. More importantly, unlike cultural values, trust and tolerance can be more readily cultivated to promote policies and workplace practices. JEL Classification G38, G41, J18, L25, M14, Z13

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.404
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.242
GPT teacher head0.383
Teacher spread0.142 · 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 teacher head, 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

Citations2
Published2025
Admission routes1
Has abstractyes

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