Bibliographic record
Abstract
Toronto continues to be a major recipient of refugee claimants and understanding the complexities of their housing trajectories is critical in ensuring they successfully integrate into society. This Major Research Paper (MRP) sets out to expand current understanding about challenges refugee claimants face in their search for permanent housing in the City of Toronto and highlight the coping strategies they have developed to navigate around these barriers. In 2011, a research team led by York University urban social geographer Valerie Preston completed a comprehensive study that compared the housing experiences of different immigrant groups, including refugee claimants, as part of a larger Pan-Canadian study. This study set the foundation for my research, as I followed the same methodological path to determine if Preston’s findings still hold value and what new trends have emerged. This paper provides a high-level overview of Toronto’s Housing Market and provides background on the current state of refugee housing in the City of Toronto to illustrate the intricacies of the local context. In order to understand the barriers, qualitative research in the form of expert interviews was also conducted with service providers who offer settlement services to refugee claimants. A total of seven interviews were conducted, transcribed and analyzed to identify themes. Interviews with service providers revealed that the most significant barriers were housing market and Affordability, employment, Toronto- specific programs, and systematic gaps. Common coping mechanisms on the other hand, included sharing accommodation and moving beyond the GTHA. Comparisons are made throughout this paper between findings from the Preston study and my research findings. Keywords: refugee claimants, refugees, Toronto, discrimination, housing affordability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".