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Record W2902979813 · doi:10.5539/jsd.v11n6p120

Water Access Today and Tomorrow: Domestic Water Sustainability under Informal Water Supply Markets in Dar es Salaam, Tanzania

2018· article· en· W2902979813 on OpenAlexvenueno aff
Francis Dakyaga, Alphonce Kyessi, Joel Msami

Bibliographic record

VenueJournal of Sustainable Development · 2018
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsnot available
FundersDeutscher Akademischer Austauschdienst
KeywordsBusinessWater supplySustainabilityInformal sectorTanzaniaEconomic growthMarketingNatural resource economicsEconomicsSocioeconomics

Abstract

fetched live from OpenAlex

The article examined domestic water access sustainability under informally driven water supply market, drawing on suppliers-consumers’ perspectives. Analysis was done on the capacities of the informal water suppliers for sustainable water provision, how the informal water markets operate, and clients’ perspectives of water access today and tomorrow. Four different data set were comparatively analyzed from interviews with the Municipal Water Engineer (MWE), Ward Health Officer (WHO), 3 Mtaa leaders from 3 informal settlements, interviews with 43 informal water sellers from three informal settlements, including a survey of (n = 292) clients in three informal settlements. It was realized that though informal water suppliers are sociocultural capable of providing flexible and gender-sensitive water supply services, households have unsustainable access to improved water due to the financial, hydro-technical, institutional and organizational in capacities of the informal water sellers coupled with the low financial abilities of low-income earning households to continuously purchase water for domestic activities. It was observed that informal settlements’ dwellers are less likely to meet their water supply needs in the near future if their income status together with the financial, hydro-technical, organizational and institutional capacities of the informal water sellers are not improved.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.264
Teacher spread0.256 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations19
Published2018
Admission routes1
Has abstractyes

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