MétaCan
Menu
Back to cohort
Record W4383041217 · doi:10.1080/1369183x.2023.2227347

The politics left behind: how pre-migration and migration experiences shape Syrian refugees’ interest in home-county politics

2023· article· en· W4383041217 on OpenAlexafffundabout
Thomas Soehl, Dietlind Stolle, Colin Scott

Bibliographic record

VenueJournal of Ethnic and Migration Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDiaspora, migration, transnational identity
Canadian institutionsConcordia UniversityMcGill UniversityCentre for Social Innovation
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRefugeePoliticsContext (archaeology)EmigrationImmigrationPolitical scienceCountry of originFamily tiesPolitical economyGender studiesSociologyDevelopment economicsLawGeographyEconomicsHistoryGenealogy

Abstract

fetched live from OpenAlex

For those fleeing violent political conflict home-country politics may be uniquely challenging. Given the high stakes which refugees are more likely to engage in home country politics? This article focuses on two sets of factors: experiences of hardship in the context of emigration, transiting and settling to their destination country; and the ongoing social ties to family and friends left behind. For our analysis, we draw on a recently collected nationally representative survey of Syrian refugees in Canada (N = 1974). We find that among those resettled in Canada, experiences of hardships in Syria and while in transit in their interim country are associated with less engagement in the political affairs of Syria. On the other hand, those who have a harder time settling into life in Canada also tend to remain more interested in home-country politics. In contrast to some findings in research on labour migrants, those who maintain close personal ties to friends and family back in Syria remain more engaged with politics. Together, the findings highlight the unique pressures refugees face and the role these pressures may have on continued interest in the political affairs of their home country after migrating.

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)0.002
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 score0.346
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.092
GPT teacher head0.390
Teacher spread0.298 · 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

Citations3
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Ethnic and Migration StudiesSame topicDiaspora, migration, transnational identityFrench-language works237,207