Academic achievement of minority home language students with special education needs in English language of instruction and French immersion programs
Bibliographic record
Abstract
Abstract This study explored the academic achievement of students who speak a minority language (ML) at home (i.e., a language other than the official languages of Canada, English and French) and who have special education needs (SEN), in two educational programs that differed in language of instruction: English language of instruction (ELoI), and Early French Immersion (EFI). The proportion of students ( n = 131) meeting the provincial standard in reading, writing, and mathematics and the effect of gender, place of birth, socio-economic status, English proficiency level, and program were analyzed. Writing was the strongest domain, followed by reading and mathematics. ML-SEN students were equally likely to meet the provincial standard whether in ELoI or EFI, and there were few significant predictors of achievement. Participating in EFI did not increase students’ risk of academic difficulty. Additional supports may be beneficial to ML-SEN students in ELoI and EFI programs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".