Towards a Bottom-up Approach for Localising SDGs in African Cities Findings from Cairo and Dar es Salaam
Bibliographic record
Abstract
This article attempts to apply a localisation methodology previously devel- oped by the authors to analyse the current status of the implementation and monitoring apparatuses for SDGs 6 (water and sanitation) and 11.2 (mobil- ity) in the case study cities – Cairo and Dar es Salaam. It uses comparative, top-down and grounded bottom-up analyses to identify gaps in the existing SDG framework and ultimately proposes a set of evaluation criteria to replace the global indicators with new localised and quantifiable indicators in the two cities. In doing so, it responds to prevalent critiques of SDGs specific to their application in the global South, including difficulties in measuring and monitoring urban conditions, misrepresentation due to the reduction of complex local conditions to abstracted data, and the inadequate capacity of the agenda to consider and assess informal activity. The proposed revisions to targets and indicators for SDG 6.1, 6.2 and 6.b, and SDG 11.2, were later discussed with community organisers and residents to bolster their validity, and represent a stepping stone towards negotiating better sustainable-development paradigms with Egyptian and Tanzanian policy-makers. More generally, these revisions invite further inquiries into other African cities or other geographies with a prominent urban informality in order to update the general SDG framework across its seventeen goals and develop locally embedded standards for different kinds of service provision and outcomes.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".