Changing Motivations or Capabilities? Migration Deterrence in the Global Context
Bibliographic record
Abstract
Abstract Over the last thirty-five years, Western liberal democracies have exerted more control over their borders through an array of innovative migration-control practices. Scholars have taken stock of these efforts and referred to them collectively as “deterrence” measures, ignoring the fact that deterrence is an established concept with a focused definition and meaning. We argue that in the context of migration governance, the concept of deterrence has been stretched beyond meaningful parameters. In order to restore conceptual clarity and develop a more useful framework, we build on the fourth wave of deterrence literature and apply its insights to these new migration-control practices. We construct a theoretically informed typology that differentiates between deterrence and defense policies. Deterrence aims to change the motivations of migrants, whereas defense policies change migrants’ capabilities. We also differentiate between the timing and location of the interventions. We elaborate on each category of policy with examples drawn from various geographic regions and propose a framework for expanding this analysis through a systematic exploration of global practices. We conclude with a discussion of the implications stemming from these insights with respect to normative and practical debates in this research area.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".