In search of a “home”: Comparing the housing challenges experienced by recently arrived Yazidi and Syrian refugees in Canada
Bibliographic record
Abstract
Abstract Housing that is affordable and appropriate is a necessity for successful integration for all newcomers. It is not uncommon for newcomers to Canada to report difficulties finding suitable, safe, and affordable housing for their families. For refugees, however, the challenges are sometimes greater. Settlement organizations and refugee sponsors experience various challenges in accommodating families with large numbers of children, but as our research shows, refugee groups have differing needs based on their culture, family composition, and experience of trauma. Using data collected from two recent studies, we identify and compare the housing needs of two newly arrived groups of refugees to Canada: Syrians and Yazidis from northern Iraq. All participants in our study have lived in Canada for 2 years or less and currently live in Alberta, Saskatchewan, Manitoba, or Ontario. Data was collected either by face-to-face surveys (with Syrian participants) or unstructured interviews (with Yazidi women) conducted in Arabic, Kurmanji, or English. We discuss their experiences of living in resettlement centers and their transition to independent housing. In addition, we discuss how family composition and previous trauma influence their housing experiences with special attention to how increasing agency increases satisfaction with housing.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".