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Record W4391290082 · doi:10.1080/1369183x.2024.2305282

Wartime (im)mobilities: effects of aspirations-capabilities on displaced Ukrainians in Canada and Germany and their viewpoints on those who remain in Ukraine

2024· article· en· W4391290082 on OpenAlexaffabout
Aryan Karimi, Yuliya Byelikova

Bibliographic record

VenueJournal of Ethnic and Migration Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMobilitiesViewpointsPolitical scienceEconomic geographySociologyGender studiesGeographySocial sciencePhysics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.876
Threshold uncertainty score0.943

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.333
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2024
Admission routes2
Has abstractyes

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