Prioritisation and Localisation of Sustainable Development Goals (SDGs): Challenges and Opportunities for Bulawayo
Bibliographic record
Abstract
Cities are engines of socio-economic development. This article examines and provides insight into the extent of localisation of the UN’s Sustainable Development Goals (SDGs) using the City of Bulawayo (CoB), in Zimbabwe, as the case study. The key question posited is ‘Does Bulawayo demonstrate potential for sustainable development?’. Bulawayo is a strange case study as in the period of the Millennium Development Goals Zimbabwe had a massive increase in death rates from 2000 to 2010 due to the HIV pandemic, political chaos and economic disintegration of that period. Coming out of that period there was little to help cities like Bulawayo grasp the opportunity for an SDG-based development focus. However, after the paper creates a multi-criteria framework from a Systematic Literature Review on the localisation of the SDG agenda, the application to Bulawayo now generates hope. The city is emerging from the collapse of the city’s public transport and water distribution systems, once the envy of and benchmark for many local authorities in the country, and has detailed SDG plans for the future. Bulawayo now serves as a planning model for localisation of sustainable development goals.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".