From Supporting States to Steering their Actions: The UN Network on Migration and the Global Compact for Migration’s Implementation
Bibliographic record
Abstract
The Global Compact for Migration (GCM) involves the establishment of an implementation mechanism that combines the periodic organisation of deliberation and information exchange between states with the routine drafting of texts and the design of governmental technologies. The GCM also tasked the UN Network on Migration (Network) with supporting the implementation mechanism in response to the needs of states. To achieve this task, the Network aimed to play a role in creating and maintaining the implementation mechanism and to leverage its expert knowledge to shape the states’ implementation efforts. This article asks how the Network has institutionalised the implementation mechanism and with what consequences for its expert authority vis-à-vis states. Drawing on insights from discursive institutionalism, the article conducts a critical discourse analysis of texts that communicate and describe the Network’s institutional work between 2019 and 2022. It demonstrates that the Network institutionalised the implementation mechanism as an experimentalist institution to bolster its expert authority and position itself as a central unit in the GCM implementation that monitors and steers the actions of states. However, the Network’s position as a central unit does not fundamentally challenge the centrality of state sovereignty in global 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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.045 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".