A First Nations approach to addressing climate change—Assessing interrelated key values to identify and address adaptive management for country
Bibliographic record
Abstract
The Yuku-Baja-Muliku (YBM) people are the Traditional Owners (First Nation People) of the land and sea country around Archer Point, in North Queensland, Australia. Our people are increasingly recognizing climate-driven changes to our cultural values and how these impact on the timing of events mapped to our traditional seasonal calendar. We invited the developers of the Climate Vulnerability Index (CVI) to our country in Far North Queensland with the aim to investigate the application of the CVI concept to assess impacts of climate change upon some of our key values. The project was the first attempt in Australia to trial the CVI process with First Nations people. By working with climate change scientists, we were able to develop a process that is Traditional Owner-centric and places our values, risk assessment, and risk mitigation and management within an established climate change assessment framework (the CVI framework). Various lessons for potential use of the CVI by other First Nation communities are outlined. Note: The authors on this paper all worked together to tell the project from a first-person narrative, which was the lead author’s voice.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| 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".