Localising the Sustainable Development Goals in Africa: implementation challenges and opportunities
Bibliographic record
Abstract
At the point of adopting the Sustainable Development Goals (SDGs), Africa’s starting point on almost all dimensions of development was much lower than that of other regions of the world. Thus, SDG progress on the continent determines to a large extent whether the global SDG commitment to ‘leave no one behind’ remains rhetoric or becomes reality. Local government action is critical to the achievement of the SDGs, as most services provided at the local level have a direct impact on SDG indicators. This paper reflects on the first quadrennial review cycle of the SDGs, and highlights challenges encountered in localising the SDGs in sub-Saharan Africa. Furthermore, the paper contributes to the ongoing strategising for the remaining timeline of the SDGs and analyses the opportunities for local governments to contribute to SDG implementation. The paper also seeks to inform policy action to strengthen local capacity to drive the SDGs agenda in the ‘Decade of Action’ (2020–2030).
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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 | 0.025 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".