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Record W3004773701

'Chasing for Water': Everyday Practices of Water Access in Peri-Urban Ashaiman, Ghana

2007· article· en· W3004773701 on OpenAlexafffund
Megan Peloso, Cynthia Morinville

Bibliographic record

VenueDigital Library Of The Commons Repository (Indiana University) · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsPeriGeographyEnvironmental planningWater resource managementSocioeconomicsEnvironmental scienceSociologyArt
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.016
GPT teacher head0.240
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations96
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueDigital Library Of The Commons Repository (Indiana University)Same topicWater Governance and InfrastructureFrench-language works237,207