Family reunion policy for resettled refugees: Governance, challenges and impacts
Bibliographic record
Abstract
The past decade has seen renewed efforts to establish resettlement as a durable solution for refugees, both as a protection tool and a mechanism to equitably distribute them among countries. Although the right to a family life is widely recognised as a fundamental human right, whether refugees can arrive with their family or be reunited with family once resettled varies across receiving countries. Little is known about family reunion policies in countries leading the resettlement efforts, and about the impact of these policies on the lives and experiences of resettled refugees. This paper addresses this gap though a systematic scoping review of academic and policy literature on family reunion policies for resettlement refugees, and on the impact of such policy on their lives. Based on a review of 42 papers published between 2010 and 2021, we outline the policies implemented in different receiving countries to enable resettled refugees to reunite with family, documenting at the same time the challenges refugees face in the process, as well as the impact of policy on their experiences. The findings evidence a tension between the refugees' own understanding of family and definitions of family in policy in receiving countries, which often results in family separation or reconfiguration. Additionally, high costs and other administrative barriers, as well as long waiting times associated with family reunification, lead to delayed or denied reunion, having detrimental effects on resettled refugees' well-being in the present and on their future prospects.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".