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Record W4391173035 · doi:10.1017/wat.2024.3

Water security risks in small, remote, indigenous communities in Canada: A critical review on challenges and opportunities

2024· review· en· W4391173035 on OpenAlexafffundabout
Michael De Coste, Sana Saleem, Haroon R. Mian, Gyan Chhipi‐Shrestha, Kasun Hewage, Madjid Mohseni, Rehan Sadiq

Bibliographic record

VenueCambridge Prisms Water · 2024
Typereview
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersMitacs
KeywordsIndigenousWater securityBusinessEnvironmental planningEnvironmental resource managementGeographyEnvironmental scienceWater resourcesEcology

Abstract

fetched live from OpenAlex

Abstract Indigenous communities in Canada are disproportionately affected by issues related to water security, especially access to clean water to meet human needs. The issues these communities face are diverse and widespread across Canada, with many causes and consequences. This review summarizes the types and magnitudes of risks associated with the water security of these communities, the consequences considering health and social perspectives, and the means of responding to these issues. Risks are broadly divided into quantitative risks (e.g., water quality and availability) and qualitative risks (e.g., lack of funding and jurisdictional conflicts). These risks lead to unique consequences, resulting in challenges in developing generalized risk response frameworks. Management of these risks includes a mix of techniques relying on legislative and technical approaches. Nevertheless, the affected communities should be included in the decision-making process that should be holistic, incorporating indigenous knowledge. Good governance, cooperation between communities, policy improvement and the development of an institutional mechanism for clean water supply will provide a pathway and guidelines to address the water security challenges among indigenous 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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.896
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.004
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 head0.326
GPT teacher head0.418
Teacher spread0.092 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2024
Admission routes3
Has abstractyes

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