Water Infrastructure for Human Settlements: Urban – Non-Urban Linkages and Water Access
Bibliographic record
Abstract
Climate change and environmental events such as higher temperatures and extended drought periods are predicted to have significant impact on water systems in human settlements in South Africa. The increasing water demands in urban areas, alongside developing townships and informal settlements in the urban periphery places further demands on already stretched water systems and creates dichotomous challenges between these spatial domains. Impoverished and marginal communities are more impacted by infrastructure deficits not just for water but including quality of housing, energy access, transport, agriculture etc. The limited institutional, resource, individual and collective social capital in these settlements also constrain the options for resilience, and the anticipatory and adaptive actions necessary to cope with current and future environmental stresses, whilst maintaining health, wellbeing and livelihood. This study examines these urban versus non-urban dichotomies as it pertains to water infrastructure resilience. It builds on the increasing realisation in theoretical and professional discourse, of the inter-dependencies between the different strata and functions of infrastructures in human settlements. Decisions, activities and actions including the policy and procedural framework governing the built and infrastructure development of settlements in one area, impacts on the other and vice versa. Thus, to achieve infrastructure resilience, the connectivity between available resources, geographical/ecological limits, technological factors, policy/institutional innovation, economic activities and socio-cultural norms must be understood and positively exploited to achieve positive transitions towards resilience. This paper utilises qualitative methods: ethnography, interviews, meeting and workshops with stakeholders in a region of South Africa, to explore the issues of resilient water infrastructure mitigation and adaptation. It was found that in addition to environmental pressures due to climate change, limited funding and infrastructure investment, institutional, social and cultural factors create a melting pot of challenges for under-served peri-urban and rural areas; causing significant outgoing migration, impact on health, sanitation, livelihood and productivity etc. The paper concludes by synthesising the findings on the need for improved transactions across political, institutional, spatial and social domains to reconcile resource and economic needs. It concludes with recommendations for addressing the inequalities in limited water resource and infrastructure capacity areas.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".