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Record W4298005908 · doi:10.31219/osf.io/2knmc

Who’s Deserving? How People Experiencing Displacement View Migrant Identity and Asylum Policy

2022· preprint· en· W4298005908 on OpenAlexaff
Margaret E. Peters, Yang‐Yang Zhou, Cybele Kappos, Thania Sanchez

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of British Columbia
FundersHarvard UniversityPrinceton University
KeywordsRefugeeEthnic groupPolitical scienceForced migrationIdentity (music)Observational studyFocus groupInternally displaced personDemographic economicsDevelopment economicsSociologyMedicineLawEconomics

Abstract

fetched live from OpenAlex

Do people who migrate due to crises identify with other refugees and migrant groups? In this paper, we examine whether the process of migration leads to a shared identity as a “migrant” or “refugee,” or whether individuals still identify mostly in terms of their home country or ethnicity. We argue that how individuals identify matters for their views on migration policy. If individuals see themselves as part of a larger migrant group, they may be more likely to support policies benefiting all migrants. However, if they do not, they are more likely to support policies that only benefit their group. We interviewed Syrians (N=819) and Iraqis (N=226) living in Turkey, Jordan, Syria, and Iraq, as well as Venezuelans (N=1612) living in Colombia. We also conducted six focus groups (N=36) and community leader interviews (N=8) with Syrians living in Istanbul, Turkey. Our study is unique in that we are able to make multiple comparisons: across displacement contexts, and between those who are legally categorized by the international community as “refugees” (Syrians and Iraqis) and those who do not fall under this legal category (Venezuelans). Using both observational questions and a conjoint experiment, we do not find that the process of migration leads to greater identification with other migrants. This has downstream consequences: individuals favor their co-nationals and people who are experiencing similar crises for priority entrance and do not favor more open borders.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.005
Scholarly communication0.0050.005
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.348
Teacher spread0.320 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2022
Admission routes1
Has abstractyes

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