Recognising the importance of shellfish to First Nations peoples, Indigenous and Traditional Ecological Knowledge in aquaculture and coastal management in Australia
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".