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Industries créatives, diversité et politiques culturelles

2017· article· fr· W2606489894 on OpenAlexvenueno aff
Felipe Verdugo

Bibliographic record

VenueInterventions économiques · 2017
Typearticle
Languagefr
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesDiversity (politics)EconomyEthnologySociologyArtEconomics

Abstract

fetched live from OpenAlex

S’intéressant à une pluralité de perspectives traitant des industries créatives, cette note de recherche poursuit deux objectifs complémentaires, mais distincts. D’une part, en guise de tour d’horizon conceptuel, la première section se penche sur les principales définitions des notions d’industries créatives, de ville créative et d’économie créative ainsi que sur diverses perspectives critiques afin de mettre en lumière certains enjeux soulevés par ces notions au niveau théorique. D’autre part, la deuxième section propose une brève analyse critique ayant pour but de discuter des défis liés autant aux politiques culturelles qu’à la mise en œuvre au niveau du commerce international de la Convention sur la protection et la promotion et de la diversité des expressions culturelles (CDEC) dans un contexte marqué par la montée de l’économie et des industries créatives.

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)0.007
metaresearch head score (Gemma)0.009
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: none
Teacher disagreement score0.020
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0050.027
Scholarly communication0.0200.009
Open science0.0010.007
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0080.001

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 head0.213
GPT teacher head0.392
Teacher spread0.179 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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