Cinéma, numérique et « multiécranicité » au Québec. Considérations empiriques et réflexives
Bibliographic record
Abstract
Le champ cinématographique a connu durant les dernières années des transformations importantes, notamment sur le plan de la diffusion des films. Ces mutations technologiques doivent être bien comprises et, surtout, situées dans leurs contextes culturels, sociaux, économiques et politiques précis, tout en évitant toute forme de déterminisme technologique. Cet article entend explorer ces questions en discutant, sur le plan conceptuel, du cinéma à l’ère des plateformes numériques et de la « multiécranicité », concept qui permet, selon nous, de comprendre certaines évolutions à l’oeuvre. Des exemples empiriques sont proposés à partir du secteur du cinéma au Québec et de ses relations avec d’autres acteurs nationaux et internationaux.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".