Between Discipline and Biopolitics: The Role of IOM and UNHCR in the Return of Crisis-Affected Populations
Bibliographic record
Abstract
The International Organization for Migration (IOM) and the United Nations High Commissioner for Refugees (UNHCR) return crisis-affected populations as part of their humanitarian interventions under the assumption that return represents a solution to the initial displacement. This assumption raises the question of how IOM and UNHCR problematize crisis-affected populations, that is, conceptualize them as a policy problem through policy documents reflecting implicit rationalities of government. Based on Foucault’s concepts of discipline and biopolitics and Bacchi’s approach to critical discourse analysis, this working paper examines the disciplinary and biopolitical rationalities of two policy documents. First, the IOM Migration Crisis Operational Framework (MCOF) draws on disciplinary rationality to outline a process of successive immobilization and mobilization of crisis-affected populations during which IOM monitors, cares for, transports, and immobilizes individuals in their countries of origin. Second, the UNHCR Policy Framework builds on a biopolitical rationality to elicit spontaneous returns of populations by reshaping the milieu of return through constructing infrastructure and restoring social services. However, each policy document combines both disciplinary and biopolitical rationalities. The MCOF strives to durably immobilize returnees by reshaping their milieu through infrastructure construction and resolution of land and property issues. The Policy Framework seeks to achieve durable returns by securing the milieu of return by restoring the nation-state’s disciplinary institutions. This complementarity between disciplinary and biopolitical rationalities indicates that IOM and UNHCR expanded their role in return governance to shape the economic, social, security and, ultimately, political conditions in the countries of return.
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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 | 0.037 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.020 | 0.134 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.009 | 0.011 |
| 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".