Transforming refugees into migrants: institutional change and the politics of international protection
Bibliographic record
Abstract
Since the 2015 refugee “crisis,” much has been made of the distinction between the legal category of refugee and migrant. While migration scholars have accounted for the increased blurring of these two categories through explanations of institutional drift and policy layering, we argue that the intentional policies utilized by states and international organizations to minimize legal avenues for refugees to seek protection should also be considered. We identify four practices of policy “conversion” that have also led to the increasingly problematic distinction between migrants and refugees: (1) limiting access to territory through burden-shifting and other practices of extraterritorialization; (2) limiting access to asylum and local integration through procedural and administrative hindrances; (3) the use of group-based criteria as a basis of exclusion; (4) the inclusion of non-Convention criteria within resettlement schemes. Drawing upon a historical institutionalist approach and a wide array of empirical sources—including 3 years of combined primary field research conducted in Egypt, Lebanon, Morocco, Tunisia, and Turkey between 2013 and 2016—we demonstrate that states are actively pursuing a greater degree of control over the selection of refugees, in practice making refugee resettlement closer to another immigration track rather than a unique status that compels state responsibility.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.048 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".