The Global Refugee Crisis: Empirical Evidence and Policy Implications for Improving Public Attitudes and Facilitating Refugee Resettlement
Bibliographic record
Abstract
The number of refugees across the globe is at an alarming high and is expected to continue to rise for the foreseeable future. As a result, finding durable solutions for refugees has become a major challenge worldwide. The literature reviewed and policy implications discussed in this article are based on the premise that one of the major solutions to the refugee crisis must be refugee resettlement in new host countries. For such a solution to succeed, however, requires relatively favorable attitudes by members of host societies, protection of the well‐being of refugees, and effective integration of refugees into new host countries. In this context, we begin by reviewing the literature on determinants of public attitudes toward refugees, the acculturation of refugees in host societies, and factors affecting refugee mental health, all of which are directly relevant to the success of the resettlement process. We then turn our attention to the policy implications of these literatures, and discuss strategies for improving public attitudes toward refugees and refugee resettlement in host countries; for improving the resettlement process to reduce mental health challenges; and for supporting the long‐term acculturation and integration of refugees in their new homes.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".