Pour faire face aux défis informationnels, numériques et médiatiques du 21e siècle
Bibliographic record
Abstract
Les fausses nouvelles sont au cœur des préoccupations sociétales. Au Québec, le Cadre de référence de la compétence numérique (Gouvernement du Québec, 2019) soutient une approche rénovée des compétences informationnelles et numériques. Ce document est notamment inspiré de la métalittératie (Mackey et Jacobson, 2011) qui a aussi influencé le plus récent référentiel de l’Association of College and Research Librairies (ACRL). Bien que la portée du concept soit limitée en français, il mérite d’être considéré. Cette note conceptuelle présente la métalittératie dans une brève chronologie et la situe dans les enjeux relatifs à la nécessité de faire évoluer les littératies. Nous concluons la réflexion en décrivant des initiatives pédagogiques.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".