Who and What Is IOM For? The Evolution of IOM’s Mandate, Policies, and Obligations
Bibliographic record
Abstract
This chapter provides an introduction to the evolution of IOM’s mandate and obligations from its founding in 1951 to 2022. In contrast to the tendency in some scholarly literature to portray IOM as a static actor devoid of normative obligations and available to unquestioningly advance state interests, however nefarious, this chapter paints a more complex picture. Focusing in particular on IOM as a “multi-mandated” organization, the chapter charts how IOM’s mandate and conceptions of its obligations have shifted over time, including in light of the development over the past two decades of a significant set of internal policies, frameworks and guidelines. Without minimizing the significant gaps and opacity that remain, the chapter explores changes in the organization’s perceived purpose and obligations, and explanations for these shifts, drawing on insights from international relations scholarship on international organizations’ legitimation efforts. Gradually, IOM has transformed from a logistics agency strapped to the interests of the United States, to a global organization with a still nascent but growing sense of its obligations not only to states but also to people on the move—changes that have ultimately advanced IOM’s efforts to secure its own position and power in the international system.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".