Tâche pour l’évaluation de la production et la compréhension de syntagmes nominaux complexes impliquant l’accord en genre en français.
Bibliographic record
Abstract
Context. Few standardized tools exist to assess vocabulary, syntax and grammar in French. Objective. This study presents a tool, made of four entertaining puzzles, that can quickly assess proficiency in the production of noun phrases with color or size adjectives. Hypothesis: We'll observe a consolidation of adjective agreement between the ages of 3 and 6, but a mastery of gender assignment in the youngest children. Method. Data from 190 French-speaking children aged 3 to 9 are presented. We report results on four tasks of increasing difficulty involving adjectives of color and size (e.g., le petit canard vert ‘the small green duck’). Results. Our results enable one to situate a child's performance in relation to his or her peers, in a variety of ways. Risk thresholds, success rates and typical errors for each one-year age range are presented, as well as at what ages the tasks should be passed by a majority of children. Morphological or syntactic errors typical (or not) of children growing up in French-speaking environments are also reported. Conclusion. These quantitative and descriptive benchmarks can inform clinical assessment of speech-language therapists, whether with the task or from language corpora, and will inform reasoning leading to clinical decisions.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".