Common But Differentiated Governance: A Metagovernance Approach to Make the SDGs Work
Bibliographic record
Abstract
The implementation of the common and universally applicable United Nations’ Sustainable Development Goals (SDGs) requires differentiated governance frameworks at all levels, as it falls short to use one governance style only—hierarchical, network or market governance—or any one style combination that is believed to be fit-for-all-purposes. The article introduces the guiding principle of “Common But Differentiated Governance” (CBDG) and illustrates how this principle can make the SDGs work. It will be shown that, after more than 15 years’ experience with the concept of “metagovernance” (how to combine different governance styles into successful governance frameworks), there seems to be some convergence towards using this as comprehensive approach to achieve situationally appropriate governance frameworks. In this article, we have elaborated how policy makers could use metagovernance, combined with key governance principles, as mechanism to support analysis, design and management of SDG governance frameworks, to make failures noticed, and to suggest mitigation measures. Metagovernance respects common principles like rule of law, but takes as starting point that there may be different pathways to achieve them. A possible step-by-step approach for SDG implementation with metagovernance is proposed, as well as establishing governance support arrangements to assist process design, review, monitoring and evaluation, at least at the national level.
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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 | 0.069 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".