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Record W4408971929 · doi:10.1016/j.erss.2025.104044

Large-scale renewable energy developments on the Indigenous Estate: How can participation benefit Australia's First Nations peoples?

2025· article· en· W4408971929 on OpenAlexaboutno aff
K. Quail, Donna Green, Ciaran O’Faircheallaigh

Bibliographic record

VenueEnergy Research & Social Science · 2025
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousEstateRenewable energyScale (ratio)Natural resource economicsGeographyBusinessEnvironmental resource managementPolitical scienceEnvironmental planningEconomicsEngineeringEcologyFinanceCartography

Abstract

fetched live from OpenAlex

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.815
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.039
GPT teacher head0.317
Teacher spread0.278 · 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.

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

Citations8
Published2025
Admission routes1
Has abstractyes

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