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Gender-Inclusive Governance and Financial Inclusion: Women Leaders in Social Enterprises

2025· article· en· W4416002118 on OpenAlexaff
Mahinda Wijesiri

Bibliographic record

VenueAcademy of Management Proceedings · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsUniversité du Québec
Fundersnot available
KeywordsCorporate governanceMicrofinanceDiversity (politics)Inclusion (mineral)Financial inclusionGender diversityRepresentation (politics)

Abstract

fetched live from OpenAlex

This study examines the relationship between board composition and the ability of social enterprises to balance social and financial inclusion objectives. Using a sample of microfinance institutions, we explore how non-profit organizations address tensions between their social missions and financial sustainability through governance mechanisms, particularly the inclusion of women on boards. Furthermore, the study delves into the influence of patriarchal cultural contexts on these dynamics, exploring their nuanced impact on board diversity and organizational priorities. The findings indicate that non-profit social enterprises exhibit a stronger commitment to financial inclusion, with women's representation on boards playing a important role in maintaining this alignment. Additionally, patriarchal norms shape the way these organizations approach gender diversity, illustrating the interaction between internal governance structures and broader cultural environments. This research enhances the literature on governance within social enterprises and provides valuable insights for promoting gender-inclusive leadership within these organizations.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0040.002
Open science0.0000.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.049
GPT teacher head0.314
Teacher spread0.265 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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