Bibliographic record
Abstract
Face à l’avènement du numérique et à la multiplication des négociations commerciales (Union européenne, États-Unis, Canada, Japon, Australie), la gouvernance mondiale des industries culturelles se trouve actuellement à un tournant. En premier lieu, le numéro de juillet traite des évolutions du débat sur l’inclusion de la culture dans l’agenda du développement durable post-2015, des initiatives dynamiques de la société civile en matière de culture, ainsi que des réticences des pays développés. En deuxième lieu, il aborde les enjeux récents des négociations commerciales en cours (Partenariat transpacifique, Partenariat transatlantique, accord sur le commerce des services) ainsi que les priorités de l’administration des États-Unis concentrées sur une inclusion dynamique des produits numériques dans l’agenda des négociations. Enfin, il présente plusieurs rapports qui mettent en lumière certains aspects de la mise en œuvre de la Convention sur la diversité des expressions culturelles (CDEC), tels que les politiques culturelles en Asie et l’impact de la CDEC, les effets normatifs de la CDEC, ainsi que son adaptation à l’ère numérique.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 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".