Who’s Deserving? How People Experiencing Displacement View Migrant Identity and Asylum Policy
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".