Innovative Approaches to Community-Based Housing for Precarious Migrants and Refugees: A Policy Report
Bibliographic record
Abstract
[para. 1]: "In 2023, Toronto’s Shelter, Support and Housing Administration reported a 440 per cent increase in refugee claimants in the shelter system since 2021 (City of Toronto 2023). In Canadian cities like Toronto, demanding conditions for rental applications and structural barriers negatively affect the ability of refugee claimants to secure housing. These challenges, plus limited financial resources, social networks, cultural familiarity, and official language familiarity, place refugee claimants at particular risk of homelessness (Kissoon 2010; Sherrell, D’Addario, and Hiebert 2007). Austerity and state withdrawal from housing provision has led to similar housing crises in other cities in North America (Ngueita 2020), Europe (Meet et al. 2021), Latin America (Magliano and Perissinotti 2020), and Africa (Paller 2015). In response, local non-profits and community-based housing organizations (CBHOs) have assumed greater responsibility in supporting unhoused refugees. However, a critical knowledge gap exists in understanding how these community-based practices of hospitality provide holistic alternatives to accommodation for precarious migrants."
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".