Revolving Doors: How Externalization Policies Block Refugees and Deflect Other Migrants across Migration Routes
Bibliographic record
Abstract
Abstract Migrant destination states of the Global North generally seek to stem irregular migration while remaining committed to refugee rights. To do so, these states have increasingly sought to externalize migration control, implicating migrant origin and transit states in managing the movement of persons across borders. But do externalization policies actually have an impact on unauthorized migration flows? If yes, do those impacts vary across different migrant categories given that both asylum seekers and other migrants can cross borders without prior authorization? We argue that these policies do have an impact on unauthorized migration flows and that those impacts are distinct for refugees and other migrants. Using data on “irregular/illegal border crossings” collected by Frontex, the Border and Coast Guard Agency of the European Union (EU), we first find that the geographical trajectories of refugees and other migrants who cross EU borders without authorization are distinct. Using a novel method to estimate whether individuals are likely to obtain asylum in 31 European destination states, we find that “likely refugees” tend to be concentrated on a single, primary migratory route while “likely irregular migrants” may be dispersed across multiple routes. Through an event study analysis of the impact of the 2016 EU–Turkey Statement, a paradigmatic example of externalization, we show that the policy primarily blocked likely refugees while deflecting likely irregular migrants to alternative routes. Our findings ultimately highlight how externalization policies may fail to prevent unauthorized entries of irregular migrants while endangering refugee protection.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".