Enhancing Policy Capacity for Better Policy Integration: Achieving the Sustainable Development Goals in a Post COVID-19 World
Bibliographic record
Abstract
The adoption of the Sustainable Development Goals (SDGs) by the UN, in 2015, established a clear global mandate for greater integrated policymaking, but there has been little consensus on how to achieve them. The COVID-19 pandemic amplified the role of policy capacity in mounting this kind of integrated policy response; however, the relationship between pre- and post-pandemic SDG efforts remains largely unexplored. In this article, we seek to address this gap through a conceptual analysis of policy integration and the capacities necessary for its application to the current SDG situation. Building on the literature on policy design, we define policy integration as the process of effectively reconciling policy goals and policy instruments and we offer a typology of policy integration efforts based on the degree of goal and instrument consistency including: policy harmonization, mainstreaming, coordination, and institutionalization. These forms of policy integration dictate the types of strategies that governments need to adopt in order to arrive at a more coherent policy mix. Following the dimensions of policy capacity by Wu et al. (2015), policy capacities are identified that are critical to ensuring successful integration. This information, thus, contributes to both academic- and policy-related debates on policy integration, by advancing conceptual clarity on the different, and sometimes, diverging concepts used in the field.
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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 | 0.042 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.012 | 0.031 |
| Scholarly communication | 0.030 | 0.037 |
| Open science | 0.002 | 0.033 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".