Refugee Integration in Canada, Europe, and the United States: Perspectives from Research
Bibliographic record
Abstract
As the number of migrants, refugees, and asylum seekers have grown worldwide, intense debate has emerged about how long and how well they integrate into host countries. Although integration is a complex process, realized differently by different groups at different times, most prior studies capture, at best, disparate parts of the process. Overcoming this limitation is a tall task because it requires data and research that capture how integration is both dynamic and contextual and requires focusing on conceptual issues, emphasizing how integration varies across spatial scales, and including perspectives of the process through the eyes of both scholars and practitioners. This article reviews recent key studies about refugees in Canada, Europe, and the United States, as a way of putting into context the scholarship presented in this special issue of The ANNALS. We analyze whether and how prior studies capture integration as a dynamic process that unfolds in various aspects of life, such as education, employment, and health. We also consider the extent to which prior studies are shaped by long-standing divides between the terms refugee and migrant, and integration and assimilation, and what those divides mean for research on refugee and migrant integration in the twenty-first century. Throughout, we assess the data needed for researchers to address a wide variety of questions about refugee integration and understand the long-term consequences of the ever-growing number of displaced persons seeking refuge. This volume presents research that uniquely enhances our understanding about the breadth of the integration process in the United States, Canada, and European countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.023 |
| Science and technology studies | 0.039 | 0.029 |
| Scholarly communication | 0.023 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".