Refugee Integration Goes Transnational: Afghans and Ukrainians Prepare for Integration in Canada Before and After Arrival
Bibliographic record
Abstract
ABSTRACT What does integration look like when immigrants and refugees mobilize socioeconomic resources before arriving in their new destination countries to proactively navigate the integration process? To date, research in refugee studies has emphasized the importance of socioeconomic capital and access to digital technologies in facilitating employment, housing and language acquisition. In comparison, our empirical insights from 80 interviews with Afghan refugee women and displaced Ukrainians in Canada point to a growing use of online tools and transnational connections in destination countries as strategies for integration. Specifically, our participants relied on online tools and social connections to search for employment and housing opportunities prior to arrival in Canada. Due to the lengthy wait times for accessing language classes upon arrival, many participants, in turn, enrolled in affordable online English classes taught by tutors located in Eastern Europe. By incorporating this transnational digital landscape into current debates on refugee integration, policy and theoretical implications, it suggests that integration is increasingly influenced by transnational and digital dynamics which extend beyond the national boundaries of origin and host countries.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".