Évolution et défis conceptuels des enquêtes au sujet des habiletés de lecture et écriture des populations adultes : quelques leçons tirées de l’histoire
Bibliographic record
Abstract
Cet article est un examen critique du cadre conceptuel des enquêtes qui mesurent le savoir-lire et écrire des populations adultes. Nous traçons l’évolution de ce type d’évaluation du 19 e siècle à nos jours, afin d’identifier les assises qui résistent à l’épreuve du temps ; par exemple, une nouvelle définition de la littératie . Nous discutons également des flous conceptuels mis en évidence à la suite de l’internationalisation des enquêtes. Ces flous donnent actuellement lieu à des interprétations inexactes de données dans les médias et lors de l’établissement des politiques linguistiques. Ces flous conceptuels invitent à repenser les dimensions linguistique et culturelle de ces enquêtes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".