First Nations peoples’ role in NRM research on private lands in the Murray Darling Basin, south-eastern Australia
Bibliographic record
Abstract
Australia’s Murray-Darling Basin is beset with complex natural resource management (NRM), particularly on private lands. Addressing these complexities requires applied ecological research, local knowledge, access to research sites, and collaboration and effective relationships between researchers, organizations, and landholders. First Nations peoples and their knowledge could also contribute significantly to address these post-colonial NRM complexities. Consequently, we examined past collaborations between First Nations knowledge holders and Western researchers engaged in Australian NRM research on public and private lands. Three of this paper’s authors were invited by a group of farmers, landholders, and community members to provide advice on how to address local NRM issues on private lands within the broader context of declining engagement between government agencies and research institutions with rural communities and landholders in the New South Wales Mid-Murray region of the Murray-Darling Basin. We collaborated with the group to identify and explore experiences with NRM policy, management, and related research through stories and lived experiences in NRM. We also identified barriers to improving biodiversity conservation and NRM research in the region, including the absence of the use of local First Nations knowledge to address NRM problems. We explored a community of practice as a means to improve relationships and collaboration between local communities that include farmers and First Nations peoples, government agencies, and research institutions in undertaking NRM research on private land. This highlighted the need for broader engagement with First Nations peoples and local rural community in participation and leadership roles in NRM research. We also explored how First Nations peoples could best participate in culturally safe and respectful ways that acknowledged their interests in co-designing and participating in relevant and effective NRM research. In response we propose a Hybrid Research Model that incorporates First Nations and Western perspectives.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.023 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".