Where do we stop for First Nations cultural heritage on the critical mineral-paved road to net-zero in Australia?
Bibliographic record
Abstract
A critical analysis of the Australian government’s efforts to address the protection of First Nations People’s cultural heritage in rapid expansion of the critical mineral industry in bid for Australia to capitalise on the transition to net-zero. The analysis considers the findings from the Joint Standing Committee on Northern Australia’s reports into the destruction of the Juukan Gorge and its recommendations to reform cultural heritage protection in Australia. It provides an overview of the relationship between the mining industry and First Nations People, including the imbalance between respective interests and bargaining positions and the need for greater resourcing and capacity to improve First Nations People’s ability to protect their interests when negotiating benefit-sharing agreements. The analysis then focuses on the Critical Minerals Strategy and the Future Made in Australia Act 2024 (Cth) as it relates to the protection of First Nations People’s cultural heritage. The analysis demonstrates that the Federal government is relying on the use of social licences to operate (SLOs) to deal with cultural heritage protection in the development of its critical mineral industry. Due to this finding, the analysis then considers how effective SLOs are as a mechanism to control and regulate corporate behaviour.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".