Refugee trajectories, imaginaries, and realities: Refugee housing in Canadian cities
Bibliographic record
Abstract
The literature on refugee trajectories in Canada suggests that over time, and despite considerable hardship during the early years of resettlement, those who enter Canada as refugees eventually attain income and housing outcomes similar to other immigrants and to their Canadian-born counterparts. These positive achievements are partially described by the concept of an immigrant effect whereby immigrants to Canada are much more likely to purchase a home than their Canadian-born counterparts given their average financial circumstances. This paper seeks to deepen our collective understanding of the integration of refugees in the Canadian housing and labour market by presenting data from the 2016 census paired with findings from a qualitative case study exploring the initial years of settlement for one group of refugees. We argue that despite considerable hardship and barriers to housing and employment, refugee families exercise constrained forms of agency which helps explain their positive trajectories in the labour and housing market over the long term. This paper contributes to the literature on refugee integration by presenting data from Montreal, Toronto and Vancouver, as well as the additional cities of Ottawa, Edmonton and Calgary. This is an important addition as less is known about the outcomes of newcomers to these cities. Drawing our on our qualitative data, we also contribute to the literature by examining the specific strategies that refugee families employ to grow their social capital and share resources within households.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.009 |
| 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".