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Record W4381512874 · doi:10.1177/10564926231181555

The <i>Social</i> Effects of Entrepreneurship on Society and Some Potential Remedies: Four Provocations

2023· article· en· W4381512874 on OpenAlexaff
Tim Weiss, Robert Eberhart, Michael Lounsbury, Andrew J. Nelson, Viólina Rindova, John W. Meyer, Patricia Bromley, Rachel Atkins, Trish Ruebottom, P. Devereaux Jennings, Dev Jennings, Madeline Toubiana, Angelique Slade Shantz, Niki Khorasani, Daniel Wadhwani, Hannah Tucker, David A. Kirsch, Brent Goldfarb, Howard E. Aldrich, Daniel P. Aldrich

Bibliographic record

VenueJournal of Management Inquiry · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsUniversity of OttawaMcMaster UniversityUniversity of Alberta
Fundersnot available
KeywordsEntrepreneurshipCapitalismNexus (standard)SociologyPower (physics)Social entrepreneurshipTheme (computing)Social sciencePolitical scienceLaw

Abstract

fetched live from OpenAlex

A rapidly growing research stream examines the social effects of entrepreneurship on society. This research assesses the rise of entrepreneurship as a dominant theme in society and studies how entrepreneurship contributes to the production and acceptance of socio-economic inequality regimes, social problems, class and power struggles, and systemic inequities. In this article, scholars present new perspectives on an organizational sociology-inspired research agenda of entrepreneurial capitalism and detail the potential remedies to bound the unfettered expansion of a narrow conception of entrepreneurship. Taken together, the essays put forward four central provocations: 1) reform the study and pedagogy of entrepreneurship by bringing in the humanities; 2) examine entrepreneurship as a cultural phenomenon shaping society; 3) go beyond the dominant biases in entrepreneurship research and pedagogy; and 4) explore alternative models to entrepreneurial capitalism. More scholarly work scrutinizing the entrepreneurship–society nexus is urgently needed, and these essays provide generative arguments toward further developing this research agenda.

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)0.009
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0050.094
Scholarly communication0.0100.011
Open science0.0020.014
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0040.000

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 head0.030
GPT teacher head0.264
Teacher spread0.234 · 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 designTheoretical or conceptual
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

Citations50
Published2023
Admission routes1
Has abstractyes

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