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Record W4399333041 · doi:10.1017/aee.2024.19

Rethinking Water Governance in the Saskatchewan River Delta Through Indigenous Relational Worldviews

2024· article· en· W4399333041 on OpenAlexaffabout
John Bosco Acharibasam, Kathryn Riley, Ranjan Datta, Elder Denise McKenzie, Elder Veronica Favel

Bibliographic record

VenueAustralian Journal of Environmental Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsUniversity of ManitobaMount Royal University
Fundersnot available
KeywordsIndigenousDeltaCorporate governanceRiver deltaSociologyPolitical sciencePublic administrationManagementEngineeringEconomicsEcology

Abstract

fetched live from OpenAlex

Abstract This study critically examines the implications of integrating Indigenous relational worldviews into the water governance framework of the Saskatchewan River Delta. Using a relational theoretical framework and community-based participatory research methodology, both Indigenous community members and non-Indigenous researchers collectively examine the negative impacts of Western water governance policies and practices on the Métis community residing in Cumberland House, located in northeast Saskatchewan, Canada. Through Indigenous traditional water story-sharing methods with Indigenous Elders and Knowledge Keepers, our focus centres on Indigenous interpretations and ways of knowing the Delta. The community highlighted the pervasive influence of power dynamics and political agendas in the governance of the Delta. As such, we emphasise the necessity of challenging settler colonial systems and structures and reinvigorating Indigenous worldviews for water governance. By doing so, we advocate for the advancement of Indigenous sovereignty and self-determination in their relationship with land and water, thereby promoting the meaningful implications of the Truth and Reconciliation Commission Calls to Action.

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)0.004
metaresearch head score (Gemma)0.003
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score0.827

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.023
Scholarly communication0.0070.003
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.284
Teacher spread0.262 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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