Description du développement de microstructures dans des récits spontanés d’une dyade plurilingue d’enfants fréquentant un centre de la petite enfance québécois
Bibliographic record
Abstract
This article describes the development of oral (verbal and non verbal) microstructures present in the spontaneous narratives of a plurilingual dyad. This dyad is composed of a French-speaking and a Spanish-speaking child. The first child is 38 months old at the first time of data collection and 43 months at the second time, 5 months later. The second child is aged 40 months at the first collection time and 45 at the second. We analyze their spontaneous narratives in regards to their smallest components, in other words, the microstructures, as well as in terms of the amount of microstructures observed in their propositions. We also analyze their spontaneous narrative in terms of complexity, especially with regard to the length and richness of their propositions through their spontaneous narratives. Our results show non-significant differences in the use of verbal and nonverbal language of these children. However, mother tongue seems to have an impact on language productivity in their spontaneous narratives, to the detriment of language complexity.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".