Sun, Smoke and Self-Determination: Evaluating Climate Initiatives as Tools for First Nations Advancement and Empowerment
Bibliographic record
Abstract
This thesis evaluates the potential for climate initiatives to empower Australia’s First Nations peoples and promote inclusive Indigenous development as Australia transitions to net-zero emissions. An interdisciplinary case study approach is employed, spanning qualitative and quantitative methods and founded upon codesign with First Nations research partners. The first case study explores large-scale renewable energy developments on Country and their potential benefits for First Nations communities. Primary interview data is used to examine benefit types, potential impacts, barriers to realisation, and strategies to overcome these barriers. The second case study explores savanna fire management programs in Arnhem Land. Climate projection data is used to examine how traditional burning indicators, documented through yarning, are likely to change in the future and the implications of this for fire management and ranger groups. Both case studies demonstrate potential for enabling economic independence and self-determination, thus promoting inclusive Indigenous development, however, actions are necessary to ensure these outcomes. To overcome barriers to First Nations’ participation and benefit from large-scale renewable energy developments on Country, recommendations include for governments to implement free, prior and informed consent; to increase funding and resourcing for First Nations groups; to introduce policies to build capacity and promote First Nations ownership of projects; for the renewable energy industry to establish cultural education and training programs; and for collaboration between governments, industry, and First Nations organisations to shift from a reactive to a proactive approach to renewable energy developments. For savanna fire management programs, findings show that, based on the maximum temperature indicator, the window for conducting right-way burning is decreasing, with the start of the season shifting later and the end of the season shifting earlier in the year. No change is found for the knock-em-down rains indicator, which implies uncoupling from the maximum temperature indicator. To ensure the future viability of savanna fire management programs and their continued promotion of inclusive Indigenous development, it is recommended that Indigenous ranger groups start considering adaptation pathways, such as increasing resources and implementing night burning to maximise use of the shortening right-way burning window.
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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 | 0.043 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".