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Record W4413755783 · doi:10.4337/apjel.2025.01.01

Where do we stop for First Nations cultural heritage on the critical mineral-paved road to net-zero in Australia?

2025· article· en· W4413755783 on OpenAlexaboutno aff

Bibliographic record

VenueAsia Pacific Journal of Environmental Law · 2025
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsNatural resource economicsCultural heritagePolitical scienceEnvironmental scienceEnvironmental ethicsLawEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.239
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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