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Record W2901693131 · doi:10.3390/w10111639

Indigenous Water Governance in Australia: Comparisons with the United States and Canada

2018· article· en· W2901693131 on OpenAlexaboutno aff
Julie H. Tsatsaros, Jennifer L. Wellman, Iris Bohnet, Jon Brodie, Peter Valentine

Bibliographic record

VenueWater · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
FundersMarine and Tropical Sciences Research Facility
KeywordsIndigenousWater resourcesCorporate governanceLegislaturePolitical scienceEnvironmental planningEnvironmental resource managementTraditional knowledgeWatershed managementIndigenous rightsPublic administrationBusinessGeographyWatershedLawEconomicsEcologyFinance

Abstract

fetched live from OpenAlex

Aboriginal participation in water resources decision making in Australia is similar when compared with Indigenous peoples’ experiences in other common law countries such as the United States and Canada; however, this process has taken different paths. This paper provides a review of the literature detailing current legislative policies and practices and offers case studies to highlight and contrast Indigenous peoples’ involvement in water resources planning and management in Australia and North America. Progress towards Aboriginal governance in water resources management in Australia has been slow and patchy. The U.S. and Canada have not developed consistent approaches in honoring water resources agreements or resolving Indigenous water rights issues either. Improving co-management opportunities may advance approaches to improve interjurisdictional watershed management and honor Indigenous participation. Lessons learned from this review and from case studies presented provide useful guidance for environmental managers aiming to develop collaborative approaches and co-management opportunities with Indigenous people for effective water resources management.

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
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.975
Threshold uncertainty score0.999

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 studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.015
GPT teacher head0.270
Teacher spread0.255 · 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

Citations18
Published2018
Admission routes1
Has abstractyes

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