Early lexical and syntactic development in Quebec French and English: implications for cross‐linguistic and bilingual assessment
Bibliographic record
Abstract
BACKGROUND: Although a number of studies have been conducted on normal acquisition in French, systematic methods for analysis of French and normative group data have been lacking. AIMS: To develop a systematic method for the analysis of language samples in Quebec French, and to provide preliminary normative data on early lexical and syntactic development in French with a comparison with English. METHODS & PROCEDURES: Language samples were collected for groups of monolingual French- and English-speaking children (n=39, age range 21-47 months) with normal language development. Coding conventions for French were developed based on similar principles as English SALT conventions. However, due to structural differences between the languages, coding of inflectional morphology was considerably more complex in French than in English. OUTCOMES & RESULTS: The French procedure provided developmentally sensitive measures of lexical and syntactic development, including mean length of utterance in morphemes and in words, and number of different words, and should be an important addition to the assessment procedures available for French. Cross-linguistic similarities and differences were noted in the language sample measures. Although the same elicitation context was used in the English and the French language samples, and the analysis methods were designed to rest on similar principles across languages, systematic differences emerged such that the French-speaking children exhibited a higher mean length of utterance, but smaller vocabulary sizes. Differences were also noted in error patterns, with much lower error rates occurring in samples of the French-speaking children. CONCLUSIONS: The findings have important implications for language assessment involving cross-linguistic comparisons, such as occurs in the assessment of bilingual children, and in the matching of participants in cross-linguistic studies. Given differences in the mean length of utterance and vocabulary scores across the languages, the finding of the same mean length of utterance or vocabulary obtained in the two languages for a given bilingual child or for monolingual speakers of the two languages does not imply equivalent levels of language development in the two languages.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".