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Emancipating the woman: how gender-mix in entrepreneurial teams leads to women’s emancipation

2016· article· en· W2766059426 on OpenAlexaff
Asma Zafar, Maria Paola Ometto

Bibliographic record

VenueAcademy of Management Proceedings · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Economic Solidarity
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEmancipationSolidarityPerspective (graphical)Citizen journalismSociologyEgalitarianismPragmatismFeminismEmpowermentSample (material)Gender studiesRepresentation (politics)Positive economicsPolitical scienceEconomic growthEconomicsEpistemologyLaw

Abstract

fetched live from OpenAlex

In this paper we ask, how gender-mix in entrepreneurial teams leads to women’s emancipation, and under what circumstances. Drawing from the pragmatist feminist perspective, we hypothesize a positive relationship between favorable representation of women in mixed gender teams, and women’s socio-cultural emancipation. We also delimit high enterprise income, and formal participatory mechanisms as two boundary conditions. Specifically, we hypothesize that high income (comparative to average regional enterprise income) and formal participatory mechanisms will enhance the formerly hypothesized main relationship. We deploy a unique sample comprising of over 5000 solidarity economy enterprises (SEEs) in the Brazilian solidarity economy movement to test our hypotheses. Analytic techniques, and paper’s contributions are also discussed.

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)0.004
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.005
Scholarly communication0.0040.002
Open science0.0000.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.036
GPT teacher head0.298
Teacher spread0.262 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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