Colonial continuities and colonial unknowing in international migration management: the International Organization for Migration reconsidered
Bibliographic record
Abstract
The International Organization for Migration (IOM) exerts increasing power in global migration governance, yet research on IOM’s early history is scarce. Explanations of IOM’s founding and early migration management efforts are often reduced to bipolar, Cold War politics, with the US creating the organisation outside the UN to sidestep Soviet interference. Such simplistic accounts fail to grapple with the ways in which its creation and early activities also reflected and entrenched legacies of colonialism and related racialized inequalities. Drawing on extensive archival research, this article analyses how colonial interests and biases also shaped IOM’s establishment, founding documents, and vacillating positions in decolonisation movements. It examines the organisation’s role in moving colonists out of newly independent states; facilitating settler colonial states’ preference for white migrants; and advancing western interests in having an international migration forum in which opposition to exclusionary policies was virtually non-existent. In particular, it questions the agency’s involvement in supporting white migration to Southern Africa in the apartheid era, and the sanitisation of such work from IOM’s institutional history. Theoretically, the article analyses these dynamics through the lens of ‘colonial unknowing’, thereby laying the foundation for deeper, historicised understandings of IOM’s continued, contested roles in migration management.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.057 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".