The role of private sponsorship on refugee resettlement outcomes: a mixed methods study of Syrians in a mid-sized city with a linguistic minority
Bibliographic record
Abstract
Given the growing number of refugees worldwide and the disproportionate burden borne by low- and middle-income host countries, the United Nations High Commissioner for Refugees has been seeking to expand pathways for refugees by relocating them to higher-income countries; as such, it put forward Canada’s program of private refugee sponsorship as a model to follow. Despite praise for Canada’s program, many countries hesitate to adopt it due to limited evidence on the integration outcomes of private refugee sponsorship. We address this knowledge gap by examining the case of Syrians resettled in a mid-sized city in Quebec, the only French-speaking province in Canada, using a mixed methods approach. We document the sponsorship experiences of Syrian refuges, and we estimate the effect of private vs. government sponsorship on their resettlement outcomes while controlling for pre-arrival characteristics. We find that private sponsorship offered refugees more diverse, intensive, enduring, and valued support compared to government sponsorship. Consistently with these results, our estimates show that private sponsorship could be an effective strategy for resettling refugees in medium-sized cities with respect to employment, housing, social networking, and a sense of belonging to the city, with the potential exception of acquiring the domestic language.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".