Political Instrumentalism and Epistemic Communities in Global Governance a Network Analysis of the International Organization for Migration
Bibliographic record
Abstract
ABSTRACT Global governance systems, including international organizations (IOs), turn to academic experts to achieve a variety of policy‐related outcomes. Existing scholarship offers valuable insights into the two main functions of expertise for international organizations–instrumental and symbolic. I draw on network analysis to propose a third function–political instrumentalism–where IOs use experts' degree of connectedness to other actors to exert influence in politicized areas of policymaking and in domestic contexts in which they are less well‐networked. To this end, IOs foster epistemic communities through networks that have the characteristics of small‐world and scale‐free networks. I illustrate this with a descriptive network analysis of the International Organization for Migration's work in migrant health. Analyzing data from IOM documentation (2016–2022), I find that IOM fosters a complex (small world and scale‐free) network through an epistemic community in which academics and researchers hold powerful positions. These positions in the network can help to serve political instrumental purposes to expand IOM's influence and visibility in domestic environments in a highly politicized area of policymaking–migrant health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".