Bibliographic record
Abstract
The objectives of this study were twofold: (1) Determine the English proficiency of English second-language learners (ELLs) at the end of preschool as referenced to monolingual norms, and in particular, to determine if they showed an asynchronous profile, that is, approached monolingual norms more closely for some linguistic sub-skills than others; (2) Investigate the role of home language environment in predicting individual differences in children’s English proficiency. Twenty-one ELL children (mean age = 58 months) from low socio-economic status (SES) backgrounds with diverse first-language backgrounds participated in the study. Children’s English proficiency was measured using a standardized story-telling instrument that yielded separate scores for their narrative, grammatical and vocabulary skills. A parent questionnaire was used to gather information about children’s home language environments. The ELL children displayed an asynchronous profile in their English development, as their standard scores varied in terms of proximity to monolingual norms; narrative story grammar was close to the standard mean, but mean length of utterance was below 1 standard deviation from the standard mean. No differences were found between the story-telling scores of the Canadian-born and foreign-born children, even though Canadian-born children were exposed to more English at home. Implications of the findings for clinicians and educators working with young ELLs are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".