‘We’re an Organization that <i>Does Stuff</i> ’: The International Organization for Migration, Logistics and Expert Authority in Migration Governance
Bibliographic record
Abstract
This article advances accounts of expertise as a source of power in migration governance by examining how the International Organization for Migration (IOM) has cultivated authority as a logistical expert. Analyses of expert authority in global governance have traditionally focused on the production and control of information, particularly research and data. In contrast, this study demonstrates that logistical expertise was pivotal to the organisation’s early successes and long-term survival, and shows how logistical prowess and values associated with the logistical frame – such as efficiency and flexibility – have underpinned IOM’s expansion in significant ways. Drawing on extensive archival research and in-depth interviews, the article traces how IOM’s logistical operations have diversified over time, from interventions explicitly intended to facilitate the movement of (selected) migrants to a contemporary focus on a much wider range of activities such as humanitarian aid, returns, and data collection, that apply logistical techniques to manage or control mobility. The contribution is two-fold. First, the article advances understanding of IOM as an increasingly influential player in global migration governance by offering a concertedly historicised perspective focused on its logistical activities and identity. Second, by bringing scholarship on expertise and critical logistics into conversation, this work illuminates how logistics functions as a form of expertise, and demonstrates the power, risks and limitations of logistics as a source of expert authority in migration governance.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".