Scan for co-signing initiatives to support refugee housing in Hamilton
Bibliographic record
Abstract
Open Homes is a multi-church network that offers home-based hospitality and wraparound supports for refugees, helping them transition to life in Canada. The network connects refugees with local families for four months, offering short-term housing while the network actively assists clients in securing long-term housing. However, refugees face significant challenges in securing long-term housing, such as limited access to affordable housing, lack of credit history or rental references, and difficulty navigating complex housing systems and legal requirements. In response to persisting difficulties refugees face in accessing stable long-term housing, Open Homes is exploring co-signer programming as a potential option to provide sustainable housing options for refugee families—an idea that emerged from previous stakeholder consultations. The cooperative co-signing program model would involve tenants, landlords, and community members willing to serve as co-signers to support refugees in meeting co-signer requirements. In Fall 2024, Open Homes partnered with Research Shop to explore similar co-signer initiatives that might exist across Canada. Over the last several months, we conducted a comprehensive environmental scan of similar housing support programs, along with key informant interviews to address questions regarding feasibility and strategic planning. We aimed to answer the following research question: What cooperative or co-signer-based housing support programs currently exist for refugees in Canada, and what features do these programs offer that could inform Open Homes' cooperative model? We also aimed to answer a secondary research question: What feasibility and risk management considerations should Open Homes consider in designing an effective co-signer program? This report presents the results of the environmental scan and findings from interviews with co-signer and long-term housing assistance programs across Canada and the United States, as well as local Hamilton stakeholders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 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 teacher head, 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".