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Record W2754715728 · doi:10.1002/wat2.1244

Sachet water: regulation and implications for access and equity in Accra, Ghana

2017· article· en· W2754715728 on OpenAlexaff
Cynthia Morinville

Bibliographic record

VenueWiley Interdisciplinary Reviews Water · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsUniversity of Toronto
FundersWorld Bank Group
KeywordsWater securityEquity (law)Universal designCommodityBusinessConsumption (sociology)Water consumptionConversationWater sectorWater industryWater resourcesEnvironmental economicsNatural resource economicsEconomic growthMarketingEnvironmental planningWater supplyWater resource managementEconomicsSociologyEngineeringGeographyFinanceSocial scienceEnvironmental sciencePolitical scienceEnvironmental engineeringEcologyLaw

Abstract

fetched live from OpenAlex

This study explores everyday lived experiences of sachet water consumption in Accra, Ghana with a particular attention to underserved areas. The aim of this focus article is twofold: to contribute to the emerging literature on sachet water by providing an account of its consumption in a city where the commodity is most ubiquitous, and to consider key research questions as the conversation around sachet water grapples with issues of regulation. The study examines the profound impact of sachet water on the municipal waterscape of Accra and focuses on the twined notions of access and equity. This study highlights how regulating the sachet water industry remains fraught with difficulties and argues that regulating the industry must begin with a commitment to universal access and must consider water needs beyond a narrow focus on drinking water.WIREs Water2017, 4:e1244. doi: 10.1002/wat2.1244 This article is categorized under: Engineering Water > Planning Water Human Water > Rights to Water Engineering Water > Sustainable Engineering of Water

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 score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.010
Scholarly communication0.0020.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.091
GPT teacher head0.442
Teacher spread0.350 · 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

Citations29
Published2017
Admission routes1
Has abstractyes

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