Academic performance and educational pathways of young allophones: A comparative multivariate analysis of Montreal, Toronto, and Vancouver
Bibliographic record
Abstract
Using several local and provincial data banks enabling one to follow the school progression of the cohort of students who, in Canada’s three main immigration-destination cities, were expected to graduate secondary school in 2004, this article examines the academic performance and educational pathways of those students who at home use a language other the main language of schooling: non-French speakers in Montreal and non-English speakers in Toronto and Vancouver. First, after accounting for differences in characteristics, those students (target group) are shown to succeed better than the remaining students (comparison group), especially in Vancouver. However, within the target group, there appear to be substantial differences in performance between linguistic subgroups, which are far from being similar in all three cities. Second, the individual and contextual factors that influence the academic performance of the students in the target group appear to be similar for some and different for others in the three cities, while presenting some more-or-less large discrepancies with the corresponding factors pertaining to the comparison group. The article concludes with a few policy implications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".