Capacity Building as Intervention-Lite: Migration Management and the Global Compacts
Bibliographic record
Abstract
Many states lack the standing capacity – housing, food, medical, or legal assistance – if thousands of people cross their border in one day. In addition, developing countries often lack the administrative capacity, expertise, and legal frameworks to process immigration or asylum applications and issue visas or refugee statuses. In response, the United Nations and other international organisations (IOs) propose to build the capacity of states through direct aid, training schemes, consultancies, twinning programmes, and start-up funds. The 2018 Global Compacts on Refugees (GCR) and the Global Compact for Safe, Orderly and Regular Migration (GCM) were in part created with the mandates to ‘build capacity’ in designated states. This article examines how the meaning of capacity building has changed in migration management over the last 70 years. First, we present a brief history of capacity building and theorise capacity building as a form of intervention-lite that relies on the invitation by the host state and reaffirms an absolutist interpretation of sovereignty. The emerging norm of ‘well-managed migration’ asserts that if a state is not able to make migration safe, then the international community has a responsibility to provide resources and training to those national institutions. The article traces this logic of intervention-lite and the norm of ‘well-managed migration’ in the Global Compacts, particularly UNHCR Asylum Capacity Support Group and UN Network on Migration’s capacity building mechanism. Methodologically, we draw on elite interviews, case studies, policy analysis, project workplans, evaluations, UN white papers and reports to examine the concept of ‘capacity building’ as framed in the Global Compacts and its implications for migration management and sovereignty. While the compacts affirm state responsibility for migration management, the GCR and GCM increase the capacity of international organisations to intervene in domestic institutions, rather than increase the capacity of national institutions.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.056 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".