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Record W2740277566

Community-Based Monitoring as a strategy of Indigenous water governance

2016· article· en· W2740277566 on OpenAlexaboutno aff
Nicole J. Wilson

Bibliographic record

VenueWestern CEDAR (Western Washington University) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousCorporate governanceEnvironmental resource managementEnvironmental planningPolitical scienceBusinessGeographyEnvironmental scienceEcology
DOInot available

Abstract

fetched live from OpenAlex

Alterations in water have significant implications for Indigenous peoples due to complex interconnections between environment, health, livelihoods and cultural well being. Indigenous peoples often express frustration with the inability to protect their complex socio-cultural relationships to water, in contexts where colonial forms of governance shape water rights and access. Yet, in spite of jurisdictional constraints, communities continue to engage multiple decolonial strategies aimed at protecting the waters within their territories. This paper analyzes community-based monitoring as one Indigenous water governance strategy. Specifically, I examine a transboundary case study of the Indigenous Observation Network – a community-based water quality monitoring network of Canadian First Nations and Alaska Native Tribes, coordinated by the Yukon River Inter-Tribal Watershed Council – in the Yukon River Basin. Analysis of semi-structured interviews with water quality samplers from across the watershed and other program partners reveal that communities value the program as it provides trusted baseline water quality data. At the same time, improvements could be made to monitor additional parameters of local concern, increase the use of data in decision-making processes and improve the sustainability of program funding.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.260
Teacher spread0.234 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2016
Admission routes1
Has abstractyes

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