Le test de closure : un outil pour mesurer l’effet de l’illustration sur la compréhension de textes
Bibliographic record
Abstract
Dans cette étude, nous examinerons l’effet de l’illustration sur la compréhension du lecteur par le biais du test de closure. Deux textes troués ont été présentés à environ 250 enfants de 3e année fréquentant l’école primaire française; ces textes étaient accompagnés ou non d’illustrations. La mesure dépendante étant le nombre de réponses identiques aux mots-cibles des textes non troués, l’effet de l’illustration s’avère significatif (p < .01). L’analyse des résultats obtenus pour chaque mot-cible montre comment on peut déterminer de façon plus précise l’effet pictural sur les mots-cibles en fonction de leurs caractéristiques syntaxiques et sémantiques et du type d’agencement verbo-iconique du texte illustré. Approximativement 20 % des mots-cibles subissent significativement (p < .05) l’effet positif ou négatif de l’illustration.
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 | 0.005 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".