SDG dilemma in local policymaking in Ghana: when ambition and reality collide
Bibliographic record
Abstract
How to implement the Sustainable Development Goals (SDGs) is a dilemma for policymakers. While the broad SDG framework provides a comprehensive blueprint for development, implementing the integrated SDGs remains a challenge in many contexts, especially where resources are limited. Moreover, employing selectivity and prioritization in implementation presents the risk of overlooking the most transformative goals. This paper examines whether local practitioners in Ghana employ grafting and pruning techniques in SDG implementation and how this selectivity and prioritization impacts SDG outcomes. We adopt a qualitative approach to analyse SDG prioritization decisions at the local government level, where essential services necessary to meet SDG targets are delivered. Local practitioners indicate that while they aspire to implement an integrated SDG framework, their implementation reality, including inadequate resources and political pressure, compels them to prioritize. Thus, practitioners prioritize basic social and economic needs and capitalize on the linkages between the SDGs to achieve multiple objectives.
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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 | 0.037 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.038 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.006 |
| 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".