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Social Ventures from a Resource–Based Perspective: An Exploratory Study Assessing Global Ashoka Fellows

2010· article· en· W2056924904 on OpenAlexaff
Moriah Meyskens, Colleen C. Robb-Post, Jeffrey A. Stamp, Alan L. Carsrud, Paul D. Reynolds

Bibliographic record

VenueEntrepreneurship Theory and Practice · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSocial capitalSocial entrepreneurshipResource (disambiguation)New VenturesKnowledge managementSample (material)BusinessEntrepreneurshipContext (archaeology)Empirical researchExploratory researchMarketingPerspective (graphical)SociologyComputer science

Abstract

fetched live from OpenAlex

This study aims to discover relationships using a resource–based view of entrepreneurship and the social value creation characteristics of 70 social entrepreneurs. This study builds on existing research that commercial and social entrepreneurs share similar operational processes by providing empirical support for these relationships from a sample of acknowledged successful social entrepreneurs and by applying a resource–based lens to the context of social entrepreneurship. Novel qualitative and quantitative content analysis techniques were applied to the online profiles of Ashoka Fellows. Statistically significant relationships were found among measures of partnerships, financial capital, innovativeness, organizational structure, and knowledge transferability. These findings suggest that social entrepreneurs, when viewed through a resource–based lens, demonstrate similar internal operational processes in utilizing resource bundles as commercial entrepreneurs.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0000.003
Research integrity0.0010.001
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 head0.032
GPT teacher head0.331
Teacher spread0.299 · 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

Citations313
Published2010
Admission routes1
Has abstractyes

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