Finding Housing for Resettled Refugees: Accounting for the Tangled Politics of Care in Canada’s Private Refugee Sponsorship Program
Bibliographic record
Abstract
This project examines private sponsors’ experiences with finding and securing housing for privately sponsored refugees (PSR) in Ottawa prior to Operation Syrian Refugee (OSR) (before November 2015); during OSR and the Syrian Refugee Resettlement Initiative (SRRI) (from November 2015 to January 2017); and after the SRRI (from January 2017 to December 2019). Although the PSR program has proved efficient in resettling newcomers in Canada, there has been little recognition of the real cost to sponsors; yet the significant amount of unpaid work these sponsors perform provides the very foundation of the program. I conducted interviews with eight private sponsors and one settlement worker between January and March 2020 to understand the challenges they face, and the social/personal networks on which they rely when navigating the housing market in the city. I also completed a literature review of publicly available information on Immigrant Serving Agency websites in Ottawa to use as a benchmark when comparing sponsorship programs approaches to housing. I demonstrate that sponsors’ deeply caring, but unpaid, voluntary work during their initial housing search often leads to significant overwork. This unpaid caring labour not only represents the very foundation at the basis of the PSR program, but it is also an outcome of the PSR program structure itself. I argue that without more of a structure of support in the PSR program – such as, better guidance or more intervention on the part of the government – sponsors’ feelings of overwork will continue unabated.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".