Refugee Integration Goes Transnational: Afghans and Ukrainians Prepare for Integration in Canada Before and After Arrival
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it