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Record W3126811001 · doi:10.18733/cpi29549

I Know Where Oysters Lie

2021· article· en· W3126811001 on OpenAlexvenueno aff
Sarah Moore

Bibliographic record

VenueCultural and Pedagogical Inquiry · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental, Ecological, and Cultural Studies
Canadian institutionsnot available
Fundersnot available
KeywordsOysterIndigenousReefValue (mathematics)Great barrier reefModalitiesWork (physics)Visual artsHistoryArt historyFisheryArtSociologyEngineeringEcologyAnthropologyComputer scienceBiology

Abstract

fetched live from OpenAlex

This research honours the Baludarri (Sydney Rock Oyster). It is interdisciplinary in its approach and showcases the work of Australian born artist, Sarah Jane Moore. It presents key findings from an artistic residency at UNSW in Sydney, Australia, through the modalities of image, song and text. It highlights the importance of the humble oyster and maps an art-meets-science approach where Moore’s creative thinking seeks inspiration from her relationship with the work of an Indigenous scientist, Laura Parker. The oyster is Moore’s living data and the work maps the deep listenings necessary to foster communities that value reefs, hold oceans as sacred and regard the oyster as a precious entity to be celebrated, protected and nourished.

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 categoriesInsufficient 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: Empirical
Teacher disagreement score0.554
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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.484
GPT teacher head0.443
Teacher spread0.041 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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