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Record W4404557749 · doi:10.1002/csr.3050

Women and leadership in non‐listed private companies in an emerging country: An analysis of barriers and facilitators

2024· article· en· W4404557749 on OpenAlexaff
Md Asadul Islam, Dieu Hack‐Polay, Mahfuzur Rahman, Justyna Fijałkowska, Francesca Dal Mas

Bibliographic record

VenueCorporate Social Responsibility and Environmental Management · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsCrandall University
Fundersnot available
KeywordsHarassmentPromotion (chess)Public relationsBusinessFocus groupPsychological interventionGender equityQualitative researchEmerging marketsMarketingPolitical sciencePsychologySociologyPoliticsSocial psychologyFinanceGender studies

Abstract

fetched live from OpenAlex

Abstract This study explores women's barriers to accessing leadership positions within non‐listed private companies in Bangladesh, drawing on the Gendered Organization Theory (GOT) as a conceptual framework. This research seeks some possible solutions to overcome these barriers. The study adopts a two‐wave qualitative methodology: semi‐structured interviews with 16 women professionals and subsequent focus‐group discussions to explore solutions. Results reveal that women face significant barriers, such as long working hours, gender pay gaps, unclear responsibilities, biased promotion processes, lack of training, and sexual harassment. These barriers are conceptualized in terms of implicit and explicit gender biases. Research participants emphasized individual efforts like self‐determination, upskilling, job switching, technology leveraging, and family support as key factors in overcoming these obstacles. The study underscores the need for organizational and governmental interventions to promote female leadership and gender equity in non‐listed companies in developing countries.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.101
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.151
GPT teacher head0.291
Teacher spread0.141 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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