Notes sur l’information culturelle et sur les industries culturelles dans une optique proprement économique
Bibliographic record
Abstract
Il s’agit, dans un premier temps, de caractériser et de situer la notion d’industrie culturelle dans le domaine plus vaste du message culturel, lui-même un sous-ensemble du champ encore plus vaste de l’information dans le secteur tertiaire de l’économie. Nous insistons ensuite sur l’opportunité d’une politique des industries culturelles qui puisse s’intégrer dans le cadre d’une politique globale et cohérente pour le Québec, face aux énormes pouvoirs culturels que détiennent les multinationales de la culture. Or, force est de constater que les données officielles de la comptabilité nationale sont peu ou pas du tout adaptées à ce champ d’activité qui, au surplus, subit une profonde mutation, imputable à la révolution technologique en cours, notamment dans le domaine de la micro-informatique.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".