The ‘Datafication’ of Borders in Global Context: The Role of the International Organization for Migration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article examines the transnational politics of datafication in migration management through a case study of the International Organization for Migration (IOM). Datafication, such as the automation of border processes, the use of biometrics and the deployment of large-scale statistical techniques, is increasingly central to the management of migration. Much of the current focus on these processes for migration management is focused on state-level policies or on supranational institutions such as the EU. There is relatively little work on the role of international organisations in this area, despite their well-researched role in setting formal standards and informal expectations around migration and mobility. The article’s first main argument is that focusing on the global level of governance yields new insights on the promotion and diffusion of data-intensive practices of migration management. The second argument is that the emerging global context of datafication around border management fits into a broader managerial agenda in which international organisations seize agenda-setting power and act as transmission mechanisms for datafication projects and ideas. The third portion of the argument is that the IOM’s projects in this area reinforce its role as a service provider and consolidate its emerging role as an orchestrator of data-driven approaches to migration governance. The article thus contributes to ongoing work on the IOM’s shifting identity as well as interdisciplinary scholarship on the role of data in migration management.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it