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Access to Water\nService Modalities in Rural Alaska:\nUnderstanding Community Experiences and Perceptions

2024· article· en· W6903416372 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsnot available
Fundersnot available
KeywordsModalitiesService (business)PerceptionQualitative researchService providerFocus groupWater industry

Abstract

fetched live from OpenAlex

Households in rural Alaska rely on various water service modalities to meet daily needs. The level of service provided impacts end-users’ ability to access and benefit from their services. Furthermore, the degree of responsibility placed on end-users for the collection, storage, and maintenance of systems varies by water service modality. Centering end-users’ experiences and preferences in water management helps ensure that water infrastructure solutions align with community needs, priorities, and capabilities. To that end, we compare end-users’ experiences with different water service modalities and perceptions of service in the Yukon-Kuskokwim Delta, one of the most underserved regions in the United States. We then explore associations between experiences and sociodemographic and geographic community characteristics to identify potential inequities or factors impacting end-users’ access to water. We conducted a qualitative content analysis of 41 semistructured interviews with 50 end-users residing in the Yukon-Kuskokwim Delta. Findings show that end-users frequently struggle with service disruptions and affordability, especially hauled water users. End-users also expressed aesthetic concerns related to the taste of chlorinated water, leading them to use potentially unsafe water service modalities for consumption. Better understanding of public perceptions allows us to center community needs when improving access to water services.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0430.000

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 head0.072
GPT teacher head0.346
Teacher spread0.274 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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