Housing and Neighbourhood Challenges of Refugee Resettlement in Declining Inner City Neighbourhoods: A Winnipeg Case Study
Bibliographic record
Abstract
Most refugees arriving in Winnipeg settle in the inner city, an area of substantive urban decline. The attraction of the area is the affordable, but poor quality, housing and proximity of service agencies. The area is characterized by unemployment, poverty, crime, and safety issues. It is also the destination of a significant influx of Aboriginal people, also seeking affordable housing and services. This study tracks refugee households over a three-year period, documents trajectories in labour force participation, income and poverty trends, neighbourhood experiences, and housing circumstances. It also examines the dynamic related to the competition for affordable housing that exists with a marginalized Aboriginal population. The picture that emerges is one of improving trajectories over time but also very difficult circumstances and sacrifices in housing and neighbourhood choices. The affects of settling in declining neighbourhoods and the competition for affordable housing complicates the resettlement process. The findings suggest a range of policy and program changes that would improve the housing circumstances of newly arrived refugees, and facilitate their resettlement and integration into a new society.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".