Wartime (im)mobilities: effects of aspirations-capabilities on displaced Ukrainians in Canada and Germany and their viewpoints on those who remain in Ukraine
Bibliographic record
Abstract
In war times, what differentiates those who manage to flee from those who remain behind? Based on 468 qualitative interview and survey responses with displaced Ukrainians’ in Canada and Germany, and the aspirations-capabilities framework, we identify how macro-level policies and individual resources and aspirations combine to shape wartime (im)mobility outcomes. Canada and Germany have relaxed their entry-stay policies to facilitate the displaced populations’ arrival while Ukraine has implemented exit restrictions for conscript men aged 18–60 and, by extension, for the family members who decide to stay with them in Ukraine. Accordingly, individuals with high pre-war migration aspirations and capital have arrived in Canada, those with mid-ranging aspirations and capital have arrived in Germany, and those with high aspirations to be with the draftees have remained in Ukraine. We make a threefold contribution to forced migration studies. We argue that war acts as an amplifier of preexisting migration aspirations for some individuals, that wartime exit restriction is a distinct example of macro-level emigration policies, and that a proactive-stay-aspirations component extends the aspirations-capabilities framework’s conceptual range.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".