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Contested Waters: Political Ontologies of Water and the Production of Risk in First Nations Water Systems

2025· article· en· W4416193770 on OpenAlexaffvenueabout
Carly Dokis, Randy Restoule, Benjamin Kelly

Bibliographic record

VenueAnthropologica · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsNipissing University
Fundersnot available
KeywordsIndigenousBlameState (computer science)PoliticsNormativeWater qualityEthnographyColonialism

Abstract

fetched live from OpenAlex

Indigenous communities in Canada are disproportionately affected by unsafe and insecure water systems. While inadequate federal funding and regulatory gaps have been identified as key barriers to the provision of safe drinking water on reserves, much less attention has been paid to the ways in which water quality risks are defined and managed by state actors, and the consequences of these rationalities and technologies of regulation for Indigenous peoples. Renewed ethnographic attention to infrastructure has called attention to the ways in which infrastructures are critical sites through which narratives, technological assemblages, ideologies, political rationalities, aesthetics, and sensory experiences are produced, encountered, and contested. Infrastructures and their administration are also deeply biopolitical projects that facilitate discipline and control. In this article, we show how water infrastructures are closely tied to ongoing colonial processes that serve to subjugate and, at times, blame Indigenous people for insecure water quality on reserves. In doing so, we interrogate the normative practices and techniques through which the Canadian state assesses water quality risks in Indigenous communities and the associated consequences for water governance.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.346

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0120.051
Scholarly communication0.0090.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.291
Teacher spread0.277 · 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 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
Published2025
Admission routes3
Has abstractyes

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