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Record W3033696405 · doi:10.5430/jms.v11n2p56

Italian Female Social Entrepreneurship and Management: An Explorative Study on Social Cooperatives

2020· article· en· W3033696405 on OpenAlexvenueno aff
Francesca Picciaia

Bibliographic record

VenueJournal of Management and Strategy · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsOriginalityEntrepreneurshipSocial entrepreneurshipFemale entrepreneursExploratory researchSocial enterpriseSample (material)Value (mathematics)PhenomenonSociologyPublic relationsBusinessPolitical scienceSocial scienceQualitative research

Abstract

fetched live from OpenAlex

Purpose: The main aim of this paper is to individuate the elements that have influenced and/or are influencing the constitution and the activity of a sample of Italian female social cooperative, and the relationship between gender and social enterprise’s internal organization.Methodology/Approach: This is an exploratory study based on a survey of a purposive sample of Italian Social Cooperatives (SCs). The selected SCs are led by women, in order to focus on motivations, constraints and opportunities behind the foundation of the social enterprise and the relationship among female gender, activity and organization.Originality/Value: Albeit with initial insights, the study can contribute, with a country-specific analysis, to the debate on the interconnections amidst institutional environment, cultural and social elements and the development of the female entrepreneurship, with a focus on third sector.Practical Implications: Research findings could help to highlight opportunities and constraints related to the phenomenon on female social 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)0.002
metaresearch head score (Gemma)0.003
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.102
GPT teacher head0.297
Teacher spread0.195 · 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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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