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Cultural Entrepreneurship Beyond ""Entrepreneurship"": Four Domains of Inquiry

2019· article· en· W2965785114 on OpenAlexaff
Christi Lockwood, Jean‐François Soublière, Marya Besharov, Tina Dacin, Brandon Lee, Elizabeth Pontikes, Viólina Rindova, Greg Fisher

Bibliographic record

VenueAcademy of Management Proceedings · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsQueen's UniversityUniversity of Alberta
Fundersnot available
KeywordsEntrepreneurshipScope (computer science)Variety (cybernetics)SociologyLegitimationEconomic geographyPublic relationsPolitical scienceEconomicsPolitics

Abstract

fetched live from OpenAlex

Culture is widely agreed to be critical for a range of innovative and entrepreneurial processes, and the notion of “cultural entrepreneurship” is gaining currency to investigate how entrepreneurial actors make innovative courses of action amenable to others. However, early work on the topic has largely remained confined to the study of new venture legitimation or resource acquisition, offering a relatively narrow understanding of “entrepreneurship.” More recently, a growing body of work has begun to illustrate how culture additionally pervades other entrepreneurial phenomena in a wide variety of empirical settings, suggesting a need to broaden the scope of what cultural entrepreneurship can explain. The primary goal of this symposium is to spur a more encompassing agenda for research at the intersection of culture, entrepreneurship, and innovation. A panel of five distinguished speakers will first review how the landscape of “cultural entrepreneurship” has evolved, and then discuss future research avenues in four distinct domains of inquiry.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.473
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.064
GPT teacher head0.309
Teacher spread0.245 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2019
Admission routes1
Has abstractyes

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