How Do Informal International Organizations Promote the Sustainable Development Goals Through Orchestration?
Bibliographic record
Abstract
ABSTRACT Informal intergovernmental organizations (IIGOs), such as G20, G7, and BRICS, have become increasingly pivotal actors in global governance. By implication, they are afforded a key role in advancing the Sustainable Development Goals (SDGs). Without a permanent secretariat, however, IIGOs govern through orchestration, relying heavily on intermediary organizations. For instance, the G20 frequently enlists the OECD to provide analytical support and to implement its Action Plan on the 2030 Agenda for Sustainable Development. This article examines the dynamics between informal and formal international organizations, exploring how IIGOs choose specific intermediaries. Using the case of the G20 and a mixed‐method approach, I examine how IIGOs select intermediaries to promote the SDGs. To do so, I extend Downie's (2022b) G20 orchestration dataset, providing a more comprehensive resource for global governance studies. I find that the G20 considers both goal alignment and the focality of the intermediaries, favoring intermediaries with common members and greater public attention. By shedding light on IIGO–intermediary dynamics, this research enhances understanding of institutional interactions within hybrid institutional complexes (HICs) and provides insights for strengthening international cooperation, which are particularly important to facilitating sustainable development.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".