Gender at the intersection with race and class in the schooling and wellbeing of immigrant-origin students
Bibliographic record
Abstract
BACKGROUND: In French-language secondary schools in Quebec, among all immigrant-origin students, those originating from South Asia have the highest dropout rate. However, girls belonging to this group consistently outperform their male peers of similar ethnic background. This stirs questions about the reasons for this relative outperformance and its linkage with overall wellbeing among these girls. METHODS: A mixed methods approach guided data collection. It involved in-depth interviews with female and male students of South Asian origin (n = 19) and with individuals holding educational roles in the lives of youth (n = 25). An additional anonymous questionnaire aggregated parent perspectives (n = 36), though this article focuses primarily on qualitative lessons. RESULTS: This article shows three main reasons for why South Asian female adolescents in Quebec French-language secondary schools outperform their male counterparts in schooling attainment: parental expectations after migration, socialization at home, and relationships at school. According to our findings, academic perseverance among these girls does not necessarily translate into their improved wellbeing or their involvement in an advantageous process of acculturation. CONCLUSIONS: This study highlights that although gender, ethnicity, and class can create an interlocking system of oppression in certain social spheres for a specific group of women, it can emerge as advantageous in other contexts for the same group. This provides educational policy makers, as well as school and community workers, with guidance and avenues for action that can promote the wellbeing of immigrant-origin girls through involvement in beneficial processes of acculturation aligned with their improved academic performance.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".