Boon or Bane?: The Hybrid Institutional Complex for the Sustainable Development Goals
Bibliographic record
Abstract
ABSTRACT This Special Section marks the tenth anniversary of the United Nations' 2030 Agenda and the Sustainable Development Goals (SDGs). Progress on the latter has been dismal, with only 17% of targets on track. The contributions to this Special Section explore the global governance of the SDGs as a Hybrid Institutional Complex (HIC): a global governance complex characterized by institutional diversity in that it combines formal intergovernmental organizations, informal intergovernmental institutions, public‐private partnerships, multistakeholder initiatives, and private transnational institutions. The HIC framework suggests that this institutional diversity can offer governance benefits, such as good substantive fit for addressing complex transboundary SDG challenges and good political fit by including a broad swathe of actors relevant for goal attainment. Yet it also highlights governance risks, including individual institutions assuming governance tasks that they are poorly suited for and powerful actors cherry‐picking goals and softer forms of governance that fit their interests. By applying the HIC concept to discrete dimensions of SDG governance and subfields, the contributions examine whether institutional diversity is driving or hindering progress. As we approach the 2030 deadline, they provide insights into the benefits and risks of HIC‐based SDG governance, offering reflections on the remaining and post‐2030 development agenda.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| 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".