Barriers and opportunities for participatory environmental upgrading: Case study of Havelock informal settlement, Durban
Bibliographic record
Abstract
Urbanisation is one of the key challenges of this decade with 68% of the global population likely to be living in urban areas by 2050. This challenge is particularly acute in sub-Saharan Africa where future towns and cities will see an influx of residents living in spontaneous human settlements. As cities struggle to keep up with the speed of growth and spread of informal settlements, associated environmental challenges such as air and water pollution and waste management have been significantly increasing posing a health risk in high density settings. Using the case of Havelock informal settlement in Durban, the authors identified key challenges associated with poor environmental conditions, lack of basic infrastructure, and the implications for settlement upgrading. The study uses mixed methods combining transect walks, priority mapping, seasonal calendar, focus group discussions and household interviews to explore the households' most challenging environmental issues faced daily by informal dwellers. This paper seeks to make the case for targeted participatory environmental infrastructure and management delivery in urban settlements in South Africa. The research also highlights the value of adopting a holistic approach to infrastructure provision to effectively enhance the living conditions of communities. Targeted participatory processes are vital to ensure that holistic infrastructure interventions are acceptable, appropriate and embedded in local communities to create sustainable habitats.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| 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".