Large-scale renewable energy developments on the Indigenous Estate: How can participation benefit Australia's First Nations peoples?
Bibliographic record
Abstract
The transition to renewable energy in Australia represents a significant opportunity for First Nations communities to benefit from developments on their land. In partnership with the Indigenous Land and Sea Corporation and the First Nations Clean Energy Network, the authors conducted research exploring this opportunity, with a specific focus on the barriers preventing First Nations from achieving these benefits and what different groups of actors could do to help overcome these barriers. In this paper we present the findings from a series of semi-structured interviews with Traditional Owners, First Nations groups, renewable energy developers and industry representatives, legal experts and other academics. We identified two groups of barriers – overarching barriers including ongoing disadvantage and a lack of funding and resourcing for First Nations groups, and barriers specific to renewable energy developments such as the absence of Indigenous free, prior and informed consent in project approval processes and unclear, non-uniform legislative frameworks. To overcome these barriers, we recommend strategies for different actors. For example, governments could implement Indigenous free, prior and informed consent in regulatory regimes and the renewable energy industry could establish cultural education and training programs for company staff. • Renewable energy developments can be beneficial for First Nations. • Benefits will not be achieved unless barriers to Indigenous participation are overcome. • We recommend strategies that different actors can implement to help overcome barriers.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".