Illustrer des données d’entretiens sur cartes sémantiques pour examiner le développement du vocabulaire de jeunes enfants
Bibliographic record
Abstract
Résumé L’évaluation du vocabulaire auprès de jeunes enfants est un défi puisque peu d’instruments tiennent compte des processus d’apprentissage du vocabulaire. S’appuyant sur les connaissances relatives au développement des champs lexicaux, cet article propose d’organiser et de présenter, sous forme de cartes sémantiques, des données recueillies à partir d’entretiens. Afin de présenter la contribution d’une telle démarche, deux de ces cartes sont exposées, illustrant le vocabulaire expressif de deux enfants de 4 et 6 ans interrogés à trois reprises, avant et après une activité portant sur les quatre saisons. Les avantages et les limites de cette démarche sont décrits et discutés, puisque cet article a comme visée de contribuer à la création de nouveaux instruments et de nouvelles méthodes, complémentaires aux instruments déjà existants afin d’évaluer le vocabulaire de jeunes enfants. Abstract The evaluation of young children’s vocabulary is a challenge because few instruments reflect the vocabulary learning process. Based on knowledge about the development of lexical fields, this article proposes to organize and present data collected from interviews with the help of semantic maps. In order to describe the contribution of such an approach, two of these semantic maps are exposed, showing the expressive vocabulary of two children of 4 and 6 years of age before and after a learning activity on the four seasons. The advantages and limits of this approach are described and discussed, since this article intends to contribute to the creation of new instruments and new methods, complementary to existing instruments assessing the vocabulary of young children.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".