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Record W4384696417 · doi:10.1177/20530196231186962

A tale of two rivers – Baaka and Martuwarra, Australia: Shared voices and art towards water justice

2023· article· en· W4384696417 on OpenAlexaff
William Brian Bates, Long Chu, Hozaus Claire, Matthew J. Colloff, Robert B. Cotton, Ruby Davies, Libby Larsen, Glenn Loughrey, Ana Manero, Virginia Marshall, Sarah Martin, Nhat Mai Nguyen, William Nikolakis, Anne Poelina, Daniel Schulz, Katherine Selena Taylor, John Williams, Paul R. Wyrwoll, R. Quentin Grafton

Bibliographic record

VenueThe Anthropocene Review · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsUniversity of British Columbia
FundersAustralian Research Council
KeywordsIndigenousNarrativeEconomic JusticeTraditional knowledgeSociologyAction (physics)LawEnvironmental ethicsMedia studiesGeographyPolitical scienceArtEcologyLiterature

Abstract

fetched live from OpenAlex

Two of Australia’s iconic river systems, Baaka in New South Wales (NSW) and Martuwarra in Western Australia (WA), are described in a narrative that connects Indigenous custodianship, bio-physical features and art, and contrasts settler law with First Law to provide multiple ways of seeing the two river systems. Our narrative is a shared response to: (1) upstream water extractions that have imposed large costs on Baaka and its peoples; and (2) threats of water extractions and developments to Martuwarra. By scribing the voices of the two river systems, we have created a space to reimagine an emerging future that connects the past and present through the concept of ‘EveryWhen’, where First Law has primacy, and where art connects Indigenous knowledges to non-Indigenous understanding. Through a dialogue process with Indigenous knowledge holders, artists and water researchers, five action processes, or journeys, are identified to guide water decision making towards water justice.

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)0.003
metaresearch head score (Gemma)0.004
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: none
Teacher disagreement score0.083
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.008
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.366
Teacher spread0.322 · 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

Citations15
Published2023
Admission routes1
Has abstractyes

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