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Record W4392194191 · doi:10.1071/mf23193

Recognising the importance of shellfish to First Nations peoples, Indigenous and Traditional Ecological Knowledge in aquaculture and coastal management in Australia

2024· article· en· W4392194191 on OpenAlexaboutno aff
Mitchell Gibbs, Laura M. Parker, Elliot Scanes, Pauline M. Ross

Bibliographic record

VenueMarine and Freshwater Research · 2024
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousShellfishTraditional knowledgeAquacultureFisheryPlanktonEstuaryEcologyGeographyInvertebrateBiologyFish <Actinopterygii>Aquatic animal

Abstract

fetched live from OpenAlex

Throughout the world, there is a growing recognition of the importance and need for incorporation of Indigenous and Traditional Ecological Knowledge (TEK) of First Nations peoples in shellfish aquaculture and coastal management. In Australia, however, the incorporation of First Nations TEK of shellfish aquaculture and coastal management is in its infancy. This is a concern because the combined perspectives of Indigenous knowledge and Western Science are needed to restore culturally and economically significant shellfish and create successful, respectful and sustainable outcomes. The aims of this perspective piece are first to describe the evidence for the importance of shellfish aquaculture and management to First Nations peoples of Australia and second to highlight the opportunity to incorporate First Nations TEK in shellfish restoration and aquaculture in Australia. Already, models of successful incorporation of TEK of shellfish exist in Aotearoa, which provide an example for incorporation of TEK of shellfish in Australia. First Nations peoples of Australia hold a deep cultural connection with shellfish and Sea Country that has persisted for millennia. If we are to appropriately sustain and restore shellfish and manage our coasts, we must incorporate First Nations TEK and views, and respect and protect their ongoing connections to Sea Country.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.947
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.153
GPT teacher head0.430
Teacher spread0.278 · 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

Citations21
Published2024
Admission routes1
Has abstractyes

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