Acquiring the language of instruction: Effect of home language experience
Bibliographic record
Abstract
Abstract The study followed 6-year-old children in Canadian French Immersion for three years to investigate the effect of home language background on acquisition of French, the language of schooling. None of the children knew French before beginning the program. French proficiency was indicated by French vocabulary and verbal fluency tasks. A language background questionnaire was used to (a) assign children to monolingual or bilingual groups and (b) provide a continuous score for degree of bilingual experience. Categorical analyses showed bilingual children had smaller English vocabulary than monolingual children when they entered the program. For French vocabulary, categorical comparisons revealed no language group differences in the first two years but higher French scores for bilingual children in the third year. In contrast, analyses of the continuous scores revealed a relation between more bilingual experience and higher French vocabulary throughout. Similarly, categorical analyses of verbal fluency results indicated no significant language group differences for either semantic or phonological fluency, but continuous analyses of semantic fluency showed an association between more bilingual experience and better outcomes in each year. These results suggest that language experience impacts progress in learning the language of schooling and that different analytic approaches reveal different aspects of the pattern.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".