Indigenous Engagement in Tropical River Research in Australia: The TRaCK Program
Bibliographic record
Abstract
The literature on scientific-Indigenous ecological knowledge collaborations rarely analyses programmatic efforts undertaken by multi-disciplinary research groups over very large geographic scales. The TRaCK (Tropical Rivers and Coastal Knowledge) research program was established to provide the science and knowledge needed by governments, industries, and communities to sustainably manage northern Australia’s rivers and estuaries. A number of policies and procedures were developed to ensure that the needs of Indigenous people of the multi-jurisdictional region were addressed and to enhance the benefits they might derive from participating in the research. An overarching Indigenous Engagement Strategy undergirded the program’s engagement activities, providing guidance on matters relating to the protection of intellectual property, negotiation of research agreements, remuneration for Indigenous expertise, and communications standards. This article reviews the achievements and shortcomings of the TRaCK experience of Indigenous engagement and highlights lessons for researchers and research organisations contemplating applied environmental science initiatives of this scale and scope.
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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 | 0.026 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".