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Record W4391889271 · doi:10.18235/0005529

Water Affordability Measures Under Multiple and Non-Exclusive Sources in Latin America and the Caribbean

2024· preprint· en· W4391889271 on OpenAlexaff
Roberto Martı́nez-Espiñeira, María Pérez Urdiales

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsLatin AmericansCoping (psychology)NormativePublic economicsBusinessDeveloping countryCaribbean regionWater qualityEconomicsEconomic growthPolitical sciencePsychology

Abstract

fetched live from OpenAlex

Standard water affordability measures that only account for expenditure on piped water are unlikely to adequately capture the situation of all consumers in developing countries, who often experience water service quality issues and must rely on coping strategies. We construct and compare a series of water affordability ratios including coping costs, and we also adjust these ratios by normative judgements about the need for coping strategies. We use nationally representative household-level data from 18 countries in Latin America and the Caribbean, providing, for the first time, a regional perspective on water affordability. We show that the share of income devoted to water expenses substantially increases when we consider coping costs, particularly affecting the bottom 20% of the income distribution. These findings should be of interest to policy makers aiming at promoting access to safe and affordable water as we also identify the characteristics associated with water affordability issues.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.174
Threshold uncertainty score0.346

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.009
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.000
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 head0.009
GPT teacher head0.188
Teacher spread0.179 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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