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Record W3191869387 · doi:10.1177/01708406211035501

Entrepreneurial imagining: How a small team of arts entrepreneurs created the world’s largest traveling carillon

2021· article· en· W3191869387 on OpenAlexaff
Sara R. S. T. A. Elias, Todd H. Chiles, Brett Crawford

Bibliographic record

VenueOrganization Studies · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of Victoria
FundersUniversity of Missouri
KeywordsSituatedFutures contractThe artsNoveltyProcess (computing)Embodied cognitionSociologyAestheticsUnconscious mindReflexivityFutures studiesEpistemologyPsychologyVisual artsSocial psychologyComputer scienceArtSocial scienceBusinessPsychoanalysisArtificial intelligence

Abstract

fetched live from OpenAlex

Although imagination has been recognized as essential to entrepreneuring, the processes by which entrepreneurs imagine and generate novelty remain insufficiently understood. To begin addressing this oversight, we propose a rhizomatic process model of entrepreneurial imagining that comprises five elements: experiencing, early creating, reaching an impasse and gestating, (re)creating and evaluating imagined futures, and choosing and enterprising. To generate this dynamic process model, we undertook an abductive, 25-month case study, guided by enactive research, to investigate how a small team of arts entrepreneurs created the world’s largest traveling carillon. Our primary contribution is to offer new theoretical insights into entrepreneurial imagining as a complex, situated, relational performance that unfolds through conscious and unconscious, self-reflective and embodied processes.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0060.008
Scholarly communication0.0050.004
Open science0.0010.004
Research integrity0.0010.002
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 head0.031
GPT teacher head0.235
Teacher spread0.204 · 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".

Quick stats

Citations26
Published2021
Admission routes1
Has abstractyes

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