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Record W4283784587 · doi:10.18584/iipj.2022.13.1.13697

Zaagtoonaa Nibi (We Love the Water):

2022· article· en· W4283784587 on OpenAlexaffvenueabout
Nicole Latulippe, Deborah McGregor

Bibliographic record

VenueInternational Indigenous Policy Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsThe Scarborough HospitalYork UniversityUniversity of Toronto
Fundersnot available
KeywordsIndigenousCorporate governanceTraditional knowledgeWater securityCommunity engagementSociologyPolitical scienceIndigenous rightsGeorgianEnvironmental planningPublic relationsEnvironmental resource managementEnvironmental ethicsBusinessGeographyWater resourcesEcology

Abstract

fetched live from OpenAlex

This paper presents Indigenous community-led, collaborative, and community-engaged water governance research with a First Nations community in the Georgian Bay and Lake Huron region in northeastern Ontario, Canada. The methodology draws on Indigenous approaches to understanding and developing knowledge and is designed to build community capacity in research and in water protection and governance. This approach recognizes existing community strengths, including traditional knowledge, experiences, perspectives, and associated cultural perspectives and values, laws, responsibilities and lived experience in relation to water. Results identify and contextualize community-held responsibilities and legal principles pertaining to water that support culturally relevant water governance and strategic planning. By synthesizing and extending previous water protection initiatives, this research meaningfully supports the community’s position and leadership on water security and governance. This, in turn, strengthens Indigenous water governance and sustainable water governance broadly as Indigenous understandings and approaches to water are holistic and concern relationships with and responsibilities to all of Creation.

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)0.002
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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.951
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0240.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.346
Teacher spread0.323 · 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
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

Citations5
Published2022
Admission routes3
Has abstractyes

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