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Record W2907374093 · doi:10.1111/gwao.12258

‘Strategic (dis)obedience’: Female entrepreneurs reflecting on and acting upon patriarchal practices

2018· article· en· W2907374093 on OpenAlexaff
Salvador Barragan, Murat Şakir Eroğul, Caroline Essers

Bibliographic record

VenueGender Work and Organization · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMarriage and Sexual Relationships
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsEmancipationObedienceAgency (philosophy)SociologyGender studiesEntrepreneurshipIdentity (music)Context (archaeology)NarrativePatriarchyPolitical scienceSocial scienceLawPolitics

Abstract

fetched live from OpenAlex

It has been suggested that entrepreneurship is a form of emancipation and social change for women. We adopt a more comprehensive view by considering micro‐emancipation at the level of both agency and identity of women entrepreneurs in patriarchal and Islamic societies. We borrow from organization studies literature to draw on the notions of the dynamic and ongoing process between dominators (i.e., men of the patriarchal family) and the dominated (i.e., women entrepreneurs). In this process micro‐emancipation and active obedience are intertwined. For this purpose, we contextualize the study in the United Arab Emirates, where men of the family regulate women's agency and identity. The men of the family are not only the gatekeepers of societal culture, but also the potential supporters for women to navigate the societal arrangements. By adopting an interpretive approach, we analyse the narratives of Emirati female entrepreneurs in their early stages of becoming an entrepreneur who engage in strategic (dis)obedience. The article contributes to the literature on micro‐emancipation in the context of gender and entrepreneurship.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0070.011
Scholarly communication0.0050.003
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.227
GPT teacher head0.407
Teacher spread0.181 · 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

Citations81
Published2018
Admission routes1
Has abstractyes

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