'Chasing for Water': Everyday Practices of Water Access in Peri-Urban Ashaiman, Ghana
Bibliographic record
Abstract
Despite recent reports suggesting that access to improved sources of drinking water is rising in Ghana, water access remains a daily concern for many of those living in the capital region. Throughout the Greater Accra Metropolitan Area (GAMA), the urban poor manage uncertainty and establish themselves in the city by leveraging a patchwork system of basic services that draws importantly from informal systems and supplies. This paper takes a case study approach, using evidence gathered from two-months of fieldwork in a peri-urban informal settlement on the fringe of Accra, to explore everyday practices involved in procuring water for daily needs that routinely lead residents outside of the official water supply system. Findings from this case study demonstrate that respondents make use of informal water services to supplement or 'patch up' gaps left by the sporadic water flow of the official service provider, currently Ghana Water Company Ltd. (GWCL). Basic water access is thus constructed through an assemblage of coping strategies and infrastructures. This analysis contributes to understandings of heterogeneity in water access by attending to the everyday practices by which informality is operationalised to meet the needs of the urban poor, in ways that may have previously been overshadowed. This research suggests, for example, that although water priced outside of the official service provider is generally higher per unit, greater security may be obtained from smaller repetitive transactions as well as having the flexibility to pursue multiple sources of water on a day-to-day basis.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".