Bibliographic record
Abstract
Si le testing adaptatif par ordinateur (TAO) possède des avantages reconnus depuis plusieurs décennies, il recèle également quelques inconvénients. Par exemple, tel que l’a déjà souligné Wainer (2000, p. xxiii), il ne serait pas très approprié d’utiliser la stratégie du TAO pour les tests à enjeux critiques (high stake tests) comme les examens. De même, puisque le nombre d’items des tests administrés selon la stratégie du TAO est limité, il est impératif de relever et, au besoin, d’éliminer les items comportant un fonctionnement différentiel (FDI). Cet article propose une procédure pour découvrir les items comportant un FDI dans le contexte spécifique du TAO. Une méthode pour distinguer les patrons de réponses atypiques dans le contexte du TAO est aussi suggérée.
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 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 | 0.021 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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; a candidate call from one teacher head, 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".