Mobilizing ‘communities of practice’ for local development and accleration of the Sustainable Development Goals
Bibliographic record
Abstract
The United Nations Sustainable Development Goals (SDGs) set out to achieve the ambitious goal of addressing all forms of poverty, fighting inequality, tackling climate change, while ensuring that no one is left behind. Five years into the implementation of the SDGs, though progress has been recorded in some places, significant challenges persist globally. In 2019, the UN Secretary-General declared a “Decade of Action” commencing in 2020 until 2030. In the light of this campaign, it is important that all effort is garnered to accelerate action towards achieving the goals. The local government level is increasingly being recognized as the key locus of development effort, particularly because the SDGs are relevant to local jurisdictions and change can be tangibly measured at smaller scales. This paper contributes to the ongoing discourse on how best to localize the global goals. Reflecting on the Ghanaian context, the paper discusses guiding principles for effective communities of practice at the local government level. Overall, the paper underlines the advantages of coordination among stakeholders, which constitute essential ingredients for accelerating action towards the SDGs especially as we commence the “Decade of Action.”
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.035 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.003 | 0.040 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".