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Record W2281017356 · doi:10.24908/ijesjp.v4i1-2.5177

Treating Water: Engineering and the Denial of Indigenous Water Rights

2015· article· en· W2281017356 on OpenAlexaffvenueabout
Travis Hnidan

Bibliographic record

VenueInternational Journal of Engineering Social Justice and Peace · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsYork University
Fundersnot available
KeywordsIndigenousSovereigntyTechnocracyWater scarcityIndigenous rightsGovernment (linguistics)State (computer science)GovernmentalityPublic administrationPolitical sciencePoliticsIdeologyCultural assimilationPolitical economySociologyLawGeographyAgricultureEcology

Abstract

fetched live from OpenAlex

In 2011, the Department of Aboriginal Affairs and Northern Development Canada released the National Assessment of First Nations Water and Wastewater Systems as prepared by Neegan Burnside Ltd. This assessment has been largely used by government, media, and Indigenous groups to point to the decrepit state of water and wastewater systems on First Nations reserves across the country, and to advance Senate Government Bill S-8 that seeks to improve conditions in these communities. In this article, I provide a critique of the National Assessment to outline its underlying assimilationist ideology and to demonstrate how technical engineering documents can have political implications. Power is wielded by technocratic discourses like engineering and, in this case, respect for Indigenous rights and sovereignty are at stake when so-called “objective” practices reflect institutional power.

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)0.008
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.071
Scholarly communication0.0060.006
Open science0.0010.007
Research integrity0.0050.005
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 head0.010
GPT teacher head0.276
Teacher spread0.265 · 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.

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

Citations6
Published2015
Admission routes3
Has abstractyes

Explore more

Same venueInternational Journal of Engineering Social Justice and PeaceSame topicIndigenous Health, Education, and RightsFrench-language works237,207