Comment nos collections peuvent‑elles aider à prémunir contre la désinformation ?
Bibliographic record
Abstract
Tiré de l’expérience d’une formation pour le personnel de bibliothèques, ce témoignage interroge les méthodes que peuvent utiliser les bibliothèques pour contrer la désinformation à travers leurs collections. Les bibliothèques, traditionnellement perçues comme des lieux de savoirs validés, doivent garantir la qualité et la fiabilité de leurs ressources et éduquer les citoyennes et citoyens à l’esprit critique face à la surabondance numérique et aux fake news. L’article présente ainsi des questions liées aux acquisitions, à l’organisation des collections et à la médiation documentaire, ainsi qu’à la connaissance du monde éditorial et scientifique. Sont proposés des outils comme la pyramide des preuves et un scepticomêtre des bibliothèques, ainsi qu’un protocole pour que les bibliothécaires aient des éléments pour réagir face à une fake news.
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 | 0.039 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.037 | 0.037 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.011 |
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".