Dénomination rapide chez les enfants déficients intellectuels : comparaison avec des enfants tout-venant
Bibliographic record
Abstract
Cette revue de litterature s’interesse a la Denomination Rapide Automatisee (DRA) ou Rapid Automatized Naming (RAN) dans sa relation avec la deficience intellectuelle. Pour la construire, nous avons utilise 16 references bibliographiques que nous avons trouvees dans les bases de donnees PubMed et Science Direct. RAN est etudie depuis les annees 70. Les tâches de RAN ont evolue depuis leur creation par Geschwind. Il existe des tâches de RAN alphanumeriques (portant sur des lettres, des chiffres) et non alphanumeriques (portant sur des objets, des configurations digitales ou des configurations canoniques de des, etc.). Le RAN est considere comme un predicteur de certaines capacites academiques ulterieures comme la lecture et les mathematiques chez les enfants tout-venant. Si d’assez nombreuses etudes se sont interessees aux performances d’enfants tout-venant a differentes tâches de RAN, tres peu ont aborde les performances d’enfants deficients intellectuels a des tâches de RAN et a leur valeur predictive.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".