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Record W4412766368 · doi:10.5130/ccs.v17.i2.9602

First Nations People and Energy Transition: How to Increase Employment in Clean Energy

2025· article· en· W4412766368 on OpenAlex
Chris Briggs, Michelle Tjondro, Rusty Langdon, Sarah Niklas, Michael Frangos, Elianor Gerrard

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCosmopolitan Civil Societies An Interdisciplinary Journal · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsnot available
FundersUniversity of Melbourne
KeywordsClean energyEnergy (signal processing)Energy transitionTransition (genetics)BusinessPolitical scienceNatural resource economicsEconomic systemEnvironmental protectionEnvironmental scienceEconomicsChemistryMathematics

Abstract

fetched live from OpenAlex

Training and employment will be a key determinant of whether the socio-economic position of First Nations peoples is improved through the energy transition, but there are few studies on how to increase First Nations employment in renewable energy. Our study, which focusses on Renewable Energy Zones in Australia, has four key findings. Firstly, employment and training mandates and incentives in government renewable energy auctions can increase First Nations employment, but a ‘coordinated flexibility’ approach is required which accommodates regional variations, differences in occupational structure between technologies and integrates First Nations businesses. Secondly, training-led initiatives have a poor job-creation record, but programs for school students and the unemployed are required to build the labour supply to meet procurement targets. Thirdly, wherever possible, demand and supply-side instruments should be integrated within clean energy programs (e.g. housing retrofits). Fourthly, complementary measures are required which resource industry to achieve targets, improve cultural safety in workplaces and build the capacity of First Nations organisations.

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.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.000
Scholarly communication0.0010.001
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 head0.012
GPT teacher head0.301
Teacher spread0.289 · 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