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Record W4406799928 · doi:10.3390/w17030334

Rethinking Indigenous Community-Led Water Sustainability: Decolonial and Relational Approaches in Western Canada

2025· article· en· W4406799928 on OpenAlexaffabout
Ranjan Datta, Jebunnessa Chapola, Kevin Lewis

Bibliographic record

VenueWater · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsUniversity of SaskatchewanUniversity of ReginaMount Royal University
Fundersnot available
KeywordsIndigenousSustainabilityPolitical scienceEnvironmental planningSociologyEnvironmental ethicsGeographyEnvironmental resource managementEnvironmental scienceEcology

Abstract

fetched live from OpenAlex

This study examines Indigenous community-led water sustainability in Western Canada through relational and decolonial lenses, addressing the interplay between traditional knowledge, environmental governance, and cultural identity. The relational and decolonial frameworks emphasize water as a living entity integral to environmental sustainability and community well-being, contrasting with extractive Western models that prioritize economic gains. Using a community-led collaborative methodology, the research engaged Elders, Knowledge-keepers, and youth in discussions and land-based activities, reinforcing intergenerational knowledge transfer. The findings showcase critical challenges to Indigenous water governance, including industrial encroachments, climate change, and colonial environmental management systems that marginalize Indigenous perspectives. These human-created challenges threaten Indigenous water quality and disrupt Indigenous sustainable governance, underlining the need for alternative, adaptive frameworks. Despite these challenges, Indigenous communities are reclaiming water sustainability through initiatives that implement traditional knowledge, cultural revitalization, and collaborative governance models. Such efforts emphasize respect, reciprocity, and stewardship, promoting long-term environmental sustainability.

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)0.001
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 score0.263
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.021
GPT teacher head0.253
Teacher spread0.232 · 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

Citations8
Published2025
Admission routes2
Has abstractyes

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