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Record W2804012917 · doi:10.1353/artv.2018.0003

Music Entrepreneurs in a Linguistic Minority Context: Effectuation as Adaptation to the Paradoxes of Digital Technologies

2018· article· en· W2804012917 on OpenAlexaffabout
Joëlle Bissonnette, Sébastien Arcand

Bibliographic record

VenueArtivate A Journal of Entrepreneurship in the Arts · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsAdaptation (eye)LinguisticsContext (archaeology)Linguistic contextSociologyPsychologyComputer scienceHistoryLinguistic analysisPhilosophy

Abstract

fetched live from OpenAlex

Digital technologies offer new possibilities to entrepreneurs in cultural industries in linguistic minority societies. Paradoxically, they also pose a threat by exacerbating the precariousness and uncertainty experienced by them. However, there is a lack of literature on the ways in which these entrepreneurs adapt to the paradoxes brought about by digital technologies. This study aims to address this gap in the literature through an analysis of 50 semi-structured interviews carried out in the music recording industry in Canadian francophone societies and in Catalonia (Spain). Using an abductive approach, we suggest that the attitudes and practices implemented by music entrepreneurs in these societies can be interpreted according to the four principles of the effectuation theory (Sarasvathy, 2001): 1) by predetermining affordable losses; 2) by harnessing contingencies rather than avoiding them; 3) by relying on strategic alliances rather than competition; and 4) by drawing on the means rather than the ends, these entrepreneurs are able to take advantage of the possibilities offered by digital technologies while mitigating the threats. Thus, our research contributes to the literature on cultural entrepreneurship by highlighting these practices and attitudes using the effectuation theory.

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)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.170
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.058
GPT teacher head0.300
Teacher spread0.242 · 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 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

Citations11
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueArtivate A Journal of Entrepreneurship in the ArtsSame topicCultural Industries and Urban DevelopmentFrench-language works237,207