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Record W2912768099 · doi:10.1108/ijge-07-2017-0036

Women’s entrepreneurship policy: a 13 nation cross-country comparison

2017· article· en· W2912768099 on OpenAlexaff
Colette Henry, Barbara Orser, Susan Coleman, Lene Foss

Bibliographic record

VenueInternational Journal of Gender and Entrepreneurship · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsEntrepreneurshipNexus (standard)OriginalityPublic policyExtant taxonValue (mathematics)Government (linguistics)Political scienceSociologyEconomic growthPublic relationsEconomicsSocial scienceQualitative researchLaw

Abstract

fetched live from OpenAlex

Purpose Government attention to women’s entrepreneurship has increased in the past two decades; however, there are few cross-cultural studies to inform policy development. This paper aims to draw on gender and institutional theory to report on the status of female-focused small and medium-sized enterprises/entrepreneurship policies and to ask how – and to what extent – do women’s entrepreneurship policies differ among countries? Design/methodology/approach A common methodological approach is used to identify gaps in the policy-practice nexus. Findings The study highlights countries where policy is weak but practice is strong, and vice versa. Research limitations/implications The study’s data were restricted to policy documents and observations of practices and initiatives on the ground. Practical implications The findings have implications for policy makers in respect of support for women’s entrepreneurship. Recommendations for future research are advanced. Originality/value The paper contributes to extant knowledge and understanding about entrepreneurship policy, specifically in relation to women’s entrepreneurship. It is also one of the few studies to use a common methodological approach to explore and compare women’s entrepreneurship policies in 13 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0020.003
Open science0.0010.000
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 head0.057
GPT teacher head0.318
Teacher spread0.261 · 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.

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

Citations64
Published2017
Admission routes1
Has abstractyes

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