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Record W3043440732 · doi:10.18192/clg-cgl.v6i2.4751

Mémoire collective dans les industries culturelles

2020· article· en· W3043440732 on OpenAlex
Julie Bérubé

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCulture and Local Governance · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Identity and Heritage
Canadian institutionsUniversité du Québec en OutaouaisUniversity of Ottawa
Fundersnot available
KeywordsCollective memoryOpenness to experienceCompromiseCollective intelligenceSociologyFace (sociological concept)Creative industriesCultural memoryCollective responsibilityPolitical sciencePsychologySocial psychologySocial scienceComputer scienceLawAnthropologyKnowledge management

Abstract

fetched live from OpenAlex


 
 
 The cultural industries participate in building collective memory because, in many cases, public decision-makers have chosen to elevate individual memories to the rank of collective memory. Cultural industries are faced with systemic discrimination (Eikhof and Warhurst, 2013), which suggests the collective memory of these industries face the same challenges. In this theoretical article, we propose a framework based on Boltanski andThévenot’s (1991, 2006) theory of justification in order to make collective memory in cultural industries more inclusive. First, we conceptualize collective memory as a compromise between the domestic and civic worlds of Boltanski and Thévenot (1991, 2006). Then, the artists and their individual memories are presented using the world of inspiration. Finally, we propose using the world of projects to make the collective memory of cultural industries more inclusive. We, therefore, propose greater openness and democratization of collective memory in the cultural industries due to the world of projects.
 
 

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.

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)0.000
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: none
Teacher disagreement score0.750
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.037
GPT teacher head0.214
Teacher spread0.177 · 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