Bibliographic record
Abstract
Dans cette contribution nous abordons l’évolution des normes déontologiques en Belgique francophone, Finlande, France, Italie, Portugal, Québec, Royaume-Uni et Suisse selon deux axes. Le premier concerne l’évolution digitale, avec les conséquences que la numérisation engendre sur les normes et leur portée, avec une attention particulière en matière d’User Generated Content et d’intelligence artificielle. Le second axe aborde les normes concernant les discriminations et la diversité, notamment en matière de genre et d’origine. Ces axes sont identifiés au départ d’une démarche inductive basée sur l’analyse comparée des différents conseils. Deux constats majeurs peuvent être tirés de cette analyse : d’une part, les conseils renforcent la responsabilité sociale du journalisme et les valeurs traditionnelles de la profession ; d’autre part, de nouvelles valeurs semblent s’affirmer, telles que la tolérance et la diversité.
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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 | 0.030 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".