Complex multilateralism: MEIs and GSMs
Bibliographic record
Abstract
The preceding four cases studies have shown that numerous changes have taken place in the MEI–GSM relationship over the past twenty years. This chapter provides a comparative analysis of these developments and assesses their significance for global governance. We argue that there is a transformation in the nature of governance conducted by MEIs as a result of their encounter with GSMs. This transformation is labelled ‘complex multilateralism’ in recognition of its movement away from an exclusively state based structure. At present the transformation primarily takes the form of institutional modification, although some policy innovation is occurring. Such changes explicitly acknowledge that actors other than states speak on behalf of the public interest. While signalling an alteration to the method of governance, it is less clear that there is a change either in the content of governing policies or in the broad interests they represent. In the short run the MEI–GSM nexus is unlikely to greatly transform institutional functions. In the longer run, there is the possibility of incremental change in the functioning and ambit of these key institutions depending upon the outcome of continued political conflict. This chapter begins by outlining the basic characteristics of complex multilateralism and its relationship to other understandings of multilateralism. It then moves on to consider how the five characteristics of complex multilateralism (varied institutional modifications, rival motivations, ambiguous results, differential state implications and socialised agenda) have manifested themselves in our case studies.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".