Between Enforcement and Precarity: Externalization and Migrant Deaths at Sea
Bibliographic record
Abstract
Abstract Much scholarship on border enforcement and loss of life asserts the relationship between enforcement and precarity. Little research attempts to correlate the two: enforcement operations and loss of life. In this article, we statistically analyse the relationship between increased border enforcement operations at sea and migrant losses of life around the EU between 2006 and 2015, and find them to be significantly and positively correlated. We also find evidence that increased enforcement leads to rerouting of migrant journeys to ‘weak spots’ in relation to borders. These findings bring empirical support to the commonly‐asserted claim by social scientists that externalization creates greater loss of life. We argue that, although discourse about interception and externalization has shifted to humanitarian rescue narratives, offshore enforcement by any other name continues to be highly correlated with migrant deaths. We then construct two datasets documenting migrant boats lost at sea and state interdiction operations since 1980. These data serve as the basis of our statistical analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".