Early School Leaving among Immigrants in Toronto Secondary Schools
Bibliographic record
Abstract
Les données du Conseil scolaire du district de Toronto sont utilisées pour déterminer quelles sont les répercussions de vivre sous le seuil de faible revenu (SFR) sur le décrochage à l'école secondaire, en tenant compte du statut de la génération d'immigrants ainsi que d'une diversité de facteurs de risques (par exemple, le pays d'origine, l'âge à l'entrée de l'école secondaire, la réussite scolaire). Les résultats ont indiqué que la mesure SFR du voisinage constituait un prédicteur significatif du décrochage scolaire, indépendamment du statut d'immigrant. L'explication du taux de décrochage des immigrants à partir du facteur de la génération n'a obtenu que peu de soutien. La région d'origine s'est avérée un prédicteur marquant du décrochage où l'on constatait des différences entre les groupes d'immigrants et entre les étudiants immigrants et les étudiants originaires du pays. While education statistics confirm that there is little difference in the dropout rates of native‐born and immigrant youth, analyses of Toronto District School Board (TDSB) data have revealed significant variation in school persistence within immigrant groups. Among newcomer youth, the decision to leave school early has been reported to be strongly influenced by socioeconomic status as well as such factors as country of origin, age at arrival, generational status, family structure, and academic performance. While living in low‐income conditions is thought to place both foreign‐ and Canadian‐born youth at risk of poor school performance and early school withdrawal, their substantially higher incidence of poverty suggests that today's immigrant youth are likely to face greater obstacles to academic success that may in turn have detrimental, long‐term consequences. This paper uses TDSB data to investigate the extent to which living below the low‐income cutoff affects the likelihood of dropping out of secondary school, while taking into account generational status as well as a variety risk factors, noted above. Policy implications 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".