Citoyenneté numérique et didactique des langues, quels points de contacts ?
Bibliographic record
Abstract
La notion de citoyenneté numérique et les appels à la mise en place, dans les systèmes éducatifs, d’une éducation à la citoyenneté numérique (ECN) se répandent. Nous proposons ici une méta-analyse de la littérature spécialisée traitant de cette notion afin, d’une part, de dresser un portrait maximal (non normatif) du citoyen usager du numérique et, d’autre part, de montrer les points de contact entre l’ECN et la didactique des langues. Concrètement, un corpus de 96 publications récentes (2016‑2020) a été soumis à une analyse thématique de contenu. Il en ressort cinq grandes catégories qui définissent le citoyen numérique. Nous avons exploré au sein de chacune d’elles les liens possibles avec la didactique des langues.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".