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Record W2217042250 · doi:10.1002/cjas.1356

Comparing social entrepreneurship across nations: An exploratory study of institutional effects

2015· article· en· W2217042250 on OpenAlexvenueno aff
Kaisu Puumalainen, Helena Sjögrén, Pasi Syrjä, Jo Barraket

Bibliographic record

VenueCanadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsEntrepreneurshipHofstede's cultural dimensions theoryGlobeEconomic geographySocioeconomic statusCorporate governanceSocial entrepreneurshipPower (physics)SociologyDemographic economicsEconomicsSocial sciencePsychologyDemographyManagementPopulation

Abstract

fetched live from OpenAlex

Abstract Institutional and cultural contexts shape social entrepreneurship differently. This paper explores the roles of culture, socioeconomic development, and governance institutions on the prevalence of social entrepreneurship. The empirical results are based on Global Entrepreneurship Monitor data, including 49 countries across the globe. The results indicate that the negative effect of the level of development holds for entrepreneurial activity in general, but there is no such effect on social entrepreneurship. Of Hofstede's ( ) cultural dimensions, power distance is negatively related to all types of entrepreneurship, and Inglehart's ( ) secular‐rational values are positively related to established entrepreneurship. Self‐expressive values relate positively with established social entrepreneurship. The results imply that the existence of social problems and/or market failures does not result in higher levels of social entrepreneurship unless the formal institutions are sufficiently developed. Copyright © 2015 ASAC. Published by John Wiley & Sons, Ltd.

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)0.006
metaresearch head score (Gemma)0.018
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0000.004
Research integrity0.0000.001
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 head0.200
GPT teacher head0.348
Teacher spread0.148 · 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

Citations62
Published2015
Admission routes1
Has abstractyes

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