Pratiques pédagogiques : comment soutenir l’attention à l’éducation préscolaire?
Bibliographic record
Abstract
Résumé: Le contenu de cet article résulte de travaux d’une recherche sur le développement de l’attention d’enfants d’âge préscolaire. Les résultats de ce texte portent sur les pratiques déclarées des enseignants et visent à mieux comprendre les pratiques pédagogiques implantées en milieu préscolaire afin de favoriser l’acquisition de comportements attentionnels. Ce texte rappelle le problème à l’étude, le cadre théorique et la méthodologie; les résultats et la discussion terminent ce texte. Des pistes de pratiques pédagogiques ciblées pour soutenir particulièrement les enfants manifestant des difficultés attentionnelles sont soulevées. Abstract: The content of this article is the result of work on research on the development of attention of preschool children. The results of this text relate to the reported practices of teachers and aim to better understand the educational practices implemented in preschool to promote the acquisition of attentional behavior. It recalls the problem to the study, the theoretical framework and methodology; Results and discussion complete text. Targeted educational practices leads to particularly support children exhibiting attentional difficulties arise.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".