Refugee Sponsorship and Family Reunification
Bibliographic record
Abstract
Abstract This article examines the choice made by resettled refugees and their sponsors to use the Private Sponsorship of Refugees Program (PSRP) in Canada to reunite families and the benefits and challenges of doing so. The timing of our study is deliberate. Global efforts are underway to encourage other states to adopt private or community sponsorship schemes, and this spread renders examination of the benefits and burdens of this form of refugee resettlement urgent. Using data we have collected via interviews of resettled refugees and sponsors in Canada, we show that family separation has a marked impact on the ability of refugees to integrate into their new home. This conclusion highlights the possibility that there are host-state imperatives that can be better served by facilitating family reunification. Furthermore, we suggest that the successful deployment of the PSRP as a tool of family reunification depends too much on the preferences and perspectives of sponsors, who may not agree that reunification is valuable, or who may not have the capacity to facilitate such reunifications. They also may struggle with the thought that they are being forced to choose among which refugees are most in need of highly scarce resettlement spots. Together, these results generate additional support for the view, which we endorse, that states should be focused on doing more to protect family unity, especially for refugee families, outside of a private sponsorship scheme.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".