Academic Achievement, Academic Self-Concept, and Academic Motivation of Immigrant Adolescents in the Greater Toronto Area Secondary Schools
Bibliographic record
Abstract
The pattern of immigration in the last few decades coupled with the tendency for ethnic differences in educational attainment that persist over subsequent immigrant generations has led to an increasing gap in academic achievement between immigrant children, who have received little or none of their education in Canada, and nonimmigrant children, who have received all of their education in Canada. Educators tend to stress the socioeconomic and cultural factors affecting immigrant adolescents' academic achievement to the exclusion of the psychological factors that are also at play in the lives of immigrant adolescents. Therefore, this study examined the impact of psychological indicators, such as academic self-concept and academic motivation, on the academic achievement of immigrant and nonimmigrant adolescents in the Greater Toronto Area secondary schools. The immigrant adolescents in this study performed as well as their nonimmigrant counterparts in English and overall school performance. The immigrant adolescents outperformed their nonimmigrant counterparts in mathematics. The immigrant adolescents had higher levels of math and school self-concepts as well as higher intrinsic and extrinsic motivation than their nonimmigrant counterparts. Math self-concept was the only predictor of math GPA for both immigrant and nonimmigrant adolescents. However, both verbal self-concept and school self-concept were the best predictors of English GPA for both immigrant and nonimmigrant adolescents. While school self-concept was the only predictor of overall GPA for nonimmigrant adolescents, the additional factors of math self-concept and extrinsic motivation-external regulation were the best predictors for immigrant adolescents.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".