Syrian Refugee Teens’ Acculturation in Canada : A Preliminary Analysis of Refugee Integration Stress and Equity Team Data at the University of Toronto
Bibliographic record
Abstract
Approximately 21,550 Syrian refugees under the age of 17 have found a home in Canada between 2015 to 2019 (IRCC 2019). They have resettled during a time with growing sentiments of Islamophobia (Beirich and Buchanan 2018) and xenophobia (Bricker 2019) across the West. The question of Islamic compatibility with the West has posited a concern among policy makers and the general public. Understanding how this group of adolescents acculturate in Canada provides a lens into Canadian multiculturalism and our ability to welcome newcomers. This paper examines how adolescent Syrian refugee males construct their identity in relation to their homeland and Canada. Additionally, it asks how the construction of identity influences their acculturation preferences. In this essay, the results displayed are based on 15 interviews conducted in 2019 with Syrian adolescent males as a member of the Refugee Integration Stress and Equity team at the University of Toronto. These findings focus on teens’ critical period of individuation, and how they navigate identity formation in this Canadian context. This paper adds to the concept of cultural contestation (O’Brien 2017) by holding it against the Canadian case. The findings suggest that most of the adolescent males follow integrationist or ethnic acculturation strategies, while Canadian multiculturalism possibly mitigates sentiments of cultural contestation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".