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Record W4413991608 · doi:10.2166/wh.2025.346

Weaving knowledge systems to eradicate drinking water crises in First Nations across Canada

2025· article· en· W4413991608 on OpenAlexaffabout
Michael O. Kehinde, Corinne J. Schuster‐Wallace, D. Fowler, Lalita Bharadwaj

Bibliographic record

VenueJournal of Water and Health · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsFraser HealthSaskatchewan Health AuthorityGlobal Institute for Water SecurityUniversity of Saskatchewan
FundersGraduate School of Biomedical Sciences, University of Texas Southwestern Medical Center
KeywordsWeavingGeographyEconomic geographyEconomic growthEnvironmental planningPolitical scienceEconomyRegional scienceEngineeringEconomics

Abstract

fetched live from OpenAlex

In Canada, First Nations (FN) are the largest of three Indigenous groups who have occupied and lived on the land for thousands of years. With a current population of about 1.1 million, universal access to safe drinking water remains a persistent problem, with advisories a norm rather than an exception in many FN communities. This study examines the Federal Government's approach to resolving the issues of long-term drinking water advisories (LTDWAs) across FN Reserves in Canada. The objective was to determine the acknowledgment and application of FN water principles and values within the federal LTDWA intervention framework. Financial and technical capacity was also explored. Results indicate that the Federal Government's approach to eradicating LTDWA in FN focuses on infrastructure technologies, overlooking other aspects of sustainable water supply, such as advanced source water protection. As such, it overlooked (1) FN water management principles and values; (2) FN strength and capacity to manage their water; and (3) First Nations' right to self-determination. It is argued that the poor attention to FN water principles and values, including the failure to address the issues of financial and technological capacity, undermines FN rights to self-determination and contributes to the continuous presence of LTDWAs in communities.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.737

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.297
Teacher spread0.278 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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