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Record W2982557492 · doi:10.1002/arco.5198

Fishing facts and phishing fictions on K'gari (Fraser Island): Archaeological discourse in a post‐truth world

2019· article· en· W2982557492 on OpenAlexaff
Andrew Costello

Bibliographic record

VenueArchaeology in Oceania/Archæology & physical anthropology in Oceania · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsImpact
Fundersnot available
KeywordsIndigenousTraditional knowledgeColonialismSociologyRelevance (law)General partnershipCultural heritageAnthropologyArchaeologyHistoryEnvironmental ethicsEthnologyPolitical scienceLawEcology

Abstract

fetched live from OpenAlex

ABSTRACT As a discipline, archaeology must explore ways to present Indigenous and scientific interpretations of the past, employing mechanisms that are effective and relevant to contemporary Indigenous people, and which communicate values for the future that are shared by Indigenous and non‐Indigenous peoples alike. Inclusive archaeological discourse and cultural heritage management can amplify First Nations voices and contribute to the public debate on the contemporary understanding of Australia's past. In developing new ways to explore archaeological relevance to First Nations people, but also working to prevent the loss of intellectual property, archaeologists in partnership with First Nations people can forge new ways to research and communicate ideas and scientific data. The contemporary story of K'gari (Fraser Island, south‐east Queensland) and the effective harnessing by Butchulla people of modern media strategies to assert their ongoing custodial and cultural rights and diminish colonial constructs imposed upon them is a powerful example of innovative resilience and positive social change.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.005
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.002
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 head0.034
GPT teacher head0.277
Teacher spread0.243 · 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 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

Citations2
Published2019
Admission routes1
Has abstractyes

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