Deportability, humanitarianism and development: neoliberal deportation and the Global Assistance for Irregular Migrants program
Bibliographic record
Abstract
Offering return assistance and financial inducements to migrants and asylum-seekers, assisted voluntary return and reintegration (AVRR) programmes are critical to the management of migration. While AVRR programmes have emerged as an area of study in their own right, little attention has been paid to the role of these schemes in the transnational politics of anti-smuggling policy. Building on insights from border studies, migration studies and security studies, this article examines the Global Assistance for Irregular Migrants (GAIM) programme. The GAIM programme is an AVRR programme funded by the Canadian government and implemented by the International Organization for Migration (IOM), which targeted Sri Lankan nationals stranded following the disruption of smuggling ventures in West Africa. This article examines how the GAIM programme framed, rationalised and obscured the practice of neoliberal deportation as a humanitarian gesture in the interests of migrants themselves. It documents and conceptualises the humanitarian claims, narratives and representations mobilised by Canada and the IOM to explain and justify the return of stranded asylum-seekers. It argues that the GAIM programme can be analysed as a form of humanitarian securitisation, which obscures the politics of anti-smuggling policy, masks the violence of deportation and legitimises the return of stranded asylum-seekers.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".