Threats, deportability and aid: The politics of refugee rentier states and regional stability
Bibliographic record
Abstract
Abstract In 2012, 2016 and 2018–2019, Pakistan threatened to expel Afghan refugees and in 2015, 2016 and 2019, Kenya threatened to demolish the Dadaab camp and expel Somali refugees. Following the threats, the governments extracted more than $300 million aid, combined. Why did these states succeed in extracting aid despite their relatively weak status and not bordering the target of their blackmail? This article first situates refugee expulsion within the literature on refugee policies, migration diplomacy and refugee rentier states. Second, in two cases – Somalis in Kenya and Afghans in Pakistan – I show how states used the threat of expulsion to construct and leverage the deportability of their refugee communities as a foreign policy tool. States used the legal uncertainty around deportability to channel threats and violence toward refugees, but the primary audience of the threats were not refugees, but the international community. Officials in Kenya and Pakistan used threats paired with six-month or one-year delays as negotiation tactics to extract aid. Surprisingly, states that were generous hosts to refugees become strategically important because of their role in providing regional stability, which turned otherwise weak states into important allies that could threaten expulsion and extract aid from superpowers.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.032 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".