Dealing With Policy Nexuses Through Policy Integration: Governance Strategies and the Policy Capacities Needed to Meet UN Sustainable Development Goals
Bibliographic record
Abstract
Abstract The UN Sustainable Development Goals (SDGs) use indicators in an attempt to foster policy integration and coherence in order to achieve transformative societal change. But the SDGs, like their predecessor Millennium Goals, have not been entirely successful in this effort. Many studies have identified continuing challenges to integrating multiple goals in this way, linked to the complex patterns of interaction between the goals and the nature of the policy systems and subsystems in which they operate. This chapter builds on the policy design literature to argue that the main aim of the SDGs is to reconcile what are otherwise incoherent policy goals and inconsistent policy instruments in a process of policy integration. This process is made more complex in the case of this kind of “super-wicked” problem in which multiple actors face time constraints across multiple policy levels, sectors and venues. It identifies four different techniques for policy integration in such policy nexuses – policy harmonization, mainstreaming, coordination, and institutionalization – and assesses their possibilities for success in the SDG case against what is possible given the nature of the nexus and the capacity of governments to deal with it. The paper contributes to the current literature on policy integration, wicked problems, and the SDGs by further conceptualizing how integrative strategies can be better designed and implemented through capacity-building efforts aimed at developing coordinative relationships within conflict-ridden, multi-actor and multilevel cross-sectoral policy domains.
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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 | 0.035 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.029 | 0.025 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".