A study on the social integration of international secondary students in Canadian high schools
Bibliographic record
Abstract
Abstract Research on the international secondary students (ISS) is scarce compared with the proliferating literature on their tertiary counterparts. This paper focuses on social integration experiences of ISS from diverse ethnic backgrounds, the undergirding macro‐, meso‐, and micro‐mechanisms, and the supports needed for their successful integration. It draws on a subset of data from a longitudinal qualitative study through an interdisciplinary conceptual framework. This study employs multiple case study designs with critical intercultural hermeneutics as an interpretive approach. Research methods involve (a) serial interviews with six ISS that spanned the 2022–2023 school year; (b) in‐depth interviews with their parents, homestays, teachers, and agents; (c) online observations of the students’ virtual communities; (d) documents; and (e) research journals. It uncovers three mechanisms undergirding ISS’ social integration challenges: lack of ethnic proximity, a departmentalised classroom system and capital defence. This study suggests that stakeholders and researchers of ISS must obviate deficit‐oriented and assimilationist perspectives, which attribute social integration challenges solely to ISS’ limited knowledge of mainstream culture and language. Instead, a paradigm shift is necessary to redefine the criteria for successful social integration as harmonious interaction with students from diverse ethno‐cultural backgrounds, encompassing both culturally dominant and minority groups, with educators playing a central role in fostering this integration.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.029 | 0.007 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".