Politique culturelle canadienne : une entente avec Netflix qui pose question !
Bibliographic record
Abstract
Décidé à soutenir la production de contenu canadien sur les nouvelles plates-formes numériques de diffusion, le gouvernement de Justin Trudeau a récemment négocié une entente de gré à gré avec l’un des géants du Web, Netflix. Première du genre, celle-ci a suscité de vives critiques et une levée de boucliers, du moins au Québec car, tandis que toutes les entreprises de diffusion en ligne du pays sont tenues de payer taxes et impôts, la multinationale américaine, elle, en est dispensée, en échange d’un investissement de 500 millions de dollars dans des productions canadiennes. Alors que la France, la Norvège, le Japon et bien d’autres pays exigeraient de soumettre ces géants américains du Web aux mêmes règles de fiscalité numérique que leurs compagnies nationales, le Canada fait-il fausse route ?
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 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".