The High‐Level Political Forum on Sustainable Development: Orchestration by Default and Design
Bibliographic record
Abstract
Abstract The High‐Level Political Forum (HLPF) on sustainable development is a central element in the emerging governance architecture for sustainable development. Established at the 2012 United Nations (UN) Conference on Sustainable Development, the HLPF has a dauntingly expansive mandate – including setting the sustainable development agenda; enhancing integration, coordination and coherence across the UN system; and following up all sustainable development goals and commitments. Yet it has been granted limited authority and few material resources. In these circumstances, the HLPF must rely on the governance strategy of ‘orchestration’: working indirectly through intermediary organizations, and using soft modes of influence to support and guide their actions. The forum's design suggests that states intended it to pursue this approach. We identify potential intermediaries and techniques of orchestration, and assess whether the HLPF can successfully act as an orchestrator.
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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 | 0.039 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".