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Record W4381799546 · doi:10.14324/111.444/ucloe.000060

Decolonising Canadian water governance: lessons from Indigenous case studies

2023· article· en· W4381799546 on OpenAlexafffundabout
Corey McKibbin

Bibliographic record

VenueUCL Open Environment · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsIndigenousSanitationCorporate governancePolitical scienceGovernment (linguistics)PraxisPoliticsEconomic growthPublic administrationEnvironmental planningSociologyBusinessGeographyEngineeringEconomicsEcologyLawEnvironmental engineering

Abstract

fetched live from OpenAlex

Meaningful lessons about decolonising water infrastructure (social, economic and political) can be learned if we scrutinise existing governance principles such as the ones provided by the Organisation for Economic Cooperation and Development in 2021’s Principles on Water Governance. Instead of using only Western frameworks to think about policy within Indigenous spheres of water, sanitation and hygiene, the Government of Canada can look to Indigenous ways of knowing to complement their understanding of how to govern areas of water, sanitation and hygiene efficiently. In this paper, the term Indigenous encompasses First Nations, Inuit and Métis populations. This paper is presented as a step out of many towards decolonising water governance in Canada, and is intended to show that it is necessary to make space for other voices in water governance. By highlighting the dangers in the case studies, three lessons are apparent: (1) there needs to be an addition of Indigenous Two-Eyed Seeing in water governance; (2) Canada must strengthen its nation-to-nation praxis with Indigenous communities; and (3) there needs to be a creation of space in water, sanitation and hygiene that fosters Indigenous voices. This is necessary such that there can be equal participation in policy conversations to mitigate existing problems and explore new possibilities.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.858
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.047
GPT teacher head0.334
Teacher spread0.287 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations13
Published2023
Admission routes3
Has abstractyes

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