Creating New Possibilities: Service Provider Perspectives on the Settlement and Integration of Syrian Refugee Youth in a Canadian Community
Bibliographic record
Abstract
From 2015 to 2017, Canada responded to the Syrian refugee crisis by welcoming over 40,000 refugees from Syria. In this 2018 study, ten service providers in the mid-sized urban community of Waterloo Region participated in semi-structured interviews, the aim of which was to learn about the ongoing needs of Syrian refugee youth. Findings indicate that 2-3 years post-arrival, these youth were still early in the settlement and integration process and despite youth's efforts and the efforts of service providers and others, systemic challenges, particularly in education and employment, continued to be key concerns. Participants identified obstacles such as segregated classes and limited resources in the secondary school system and a variety of barriers to employment that youth faced while still learning the language and Canadian culture. Social engagement and mental health were also identified as areas for enhancement and, at the same time, were areas where youth often showed considerable resilience. The study documents the need for ongoing investment in Syrian refugee youth and continued advocacy at community and larger systems levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.054 | 0.016 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| 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".