Introducing Organizational (Dis)Entanglements: How Scholarship on Regime Complexity and Power Dynamics Helps Make Sense of International Order-Making
Bibliographic record
Abstract
Abstract Scholars and pundits focusing on the changing international order and its possible fragmentation often pay little attention to the manifold relationships between international organizations (IOs). Neglecting inter-organizational relationships, we argue, biases discussions towards doomsday predictions and reinforces the perception of global fragmentation. In this Forum, we address these biases by bringing together two strands of IR scholarship: power rivalry/transition and regime complexity. We do so by introducing the concept of organizational (dis)entanglements. An examination of how more and less powerful national and international policymakers engage and disengage IOs, highlights processes of reinforcing, muddling through, or undermining various ongoing order-making initiatives. The individual contributions examine organizational (dis)entanglements by highlighting actors’ various multilateral order-making attempts across IOs, global and regional ordering dynamics through IOs, and the roles international bureaucrats play in these processes. These contributions help identify new directions of inquiry in the study of IOs and international order by, for example, demonstrating that actors can engage with competition and cooperation simultaneously. Not all ordering attempts are equally likely to radically change global politics.
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.065 |
| Scholarly communication | 0.016 | 0.029 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".