Using Multiple Regression to Predict Minority Children's Second Language Performance
Bibliographic record
Abstract
This study examined the influence of several variables (supplementary mother-tongue instruction, the child's length of residence in the host country, self-esteem, and schools) on the French oral comprehension and expression of minority language children in Montreal. One hundred and thirty-seven elementary-level, first generation immigrant children, representing 18 different mother tongues and 36 different countries of birth, took part in the study. Results indicate that supplementary mother-tongue instruction did not signifi-cantly predict language performance. This finding, rather than a negative pro-nouncement on mother-tongue instruction, is more likely a reflection of the quality and type of variable under investigation. Schools and length of residence were both positive predictors of comprehension and expression; self-esteem significantly predicted expression, but not comprehension. The reported findings have implications for classroom educators and school authorities who are overseeing the education of rapidly growing numbers of minority language children being educated in a language which is not their mother tongue.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".