First Nations at the forefront: The changing landscape of clean energy agreements in Australia
Bibliographic record
Abstract
The clean energy transition has the potential to be very beneficial for the Australian First Nations people on whose Country much of it will occur. This paper documents results of interviews with legal and financial experts who have very particular insight into the contents of benefits agreements currently being negotiated with First Nations groups for large scale clean energy developments – agreements which are conventionally confidential. The results of our analysis give reason for cautious optimism in this space, confirming that First Nations people in Australia have the legal ability to veto clean energy projects on Country. We note the wider impacts of this emergent power of veto, which makes consent more valuable to developers, but also might encourage developers to avoid First Nations Country altogether. We further observe that as First Nations groups become key stakeholders, or co-owners, in these kinds of development, they also can become exposed to significant financial risk. The need to access excellent advice for First Nations groups in Australia who are navigating these projects – as developers, co-owners, shareholders, board members and contractors – is more urgent than ever.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.025 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".