Bibliographic record
Abstract
Résumé Il a fallu plus de vingt ans pour que l’on reconnaisse le concept de littératie en français. Outre des arguments de terminologie et d’orthographe, les opposants faisaient généralement valoir la redondance avec le concept d’alphabétisation que l’usage avait établi comme la traduction de literacy . Paradoxalement cette position était soutenue par certains qui, par ailleurs, faisaient la promotion de l’approche Whole-Language . Or, l’un des fondements de cette approche est justement le rejet des conceptions traditionnelles d’enseignement de l’écrit qui dérivent du concept d’alphabétisation. Aujourd’hui que l’approche Whole-Language est remise en question, doit-on donner raison à ses opposants et revenir aux conceptions traditionnelles ? Comme nous tenterons de le montrer dans cet article, ce serait là méconnaître les fondements épistémologiques qui sous-tendent le débat des méthodes et ignorer les leçons de l’histoire.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".