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Record W4415642762 · doi:10.1016/j.erss.2025.104417

Youth engagement in community renewable energy in northern and Indigenous communities: Lessons from Peter Ballantyne Cree Nation and Frog Lake First Nation, Canada

2025· article· en· W4415642762 on OpenAlexafffundabout
Arwa Jaradat, Bram Noble, Greg Poelzer

Bibliographic record

VenueEnergy Research & Social Science · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsUniversity of Saskatchewan
FundersSocial Sciences and Humanities Research Council of CanadaFederation for the Humanities and Social Sciences
KeywordsIndigenousCommunity engagementRenewable energyEnergy (signal processing)Environmental impact of the energy industryTraditional knowledge

Abstract

fetched live from OpenAlex

Transitioning from centralized, fossil fuel-based energy systems to decentralized renewable energy is critical for addressing climate change and ensuring energy security. Youth are key actors in the global energy transition, but there is limited understanding of the role of youth as energy citizens in rural and remote communities – especially Indigenous communities. This study examines the role of Indigenous youth in community renewable energy transition, drawing on semi-structured interviews with community members from Peter Ballantyne Cree Nation in Saskatchewan and Frog Lake First Nation, Alberta, Canada. Results show that notwithstanding the rise of youth engagement in the international energy space, the role of Indigenous youth participation in community energy transition is limited. Facilitating Indigenous youth engagement in local energy transitions requires intentionality, including formal means and opportunities to engage in energy governance; improved understanding of youths' envisioned social value of energy; addressing many of the enduring social challenges that disproportionately impact Indigenous youth; and integrating Indigenous culture and land-based learning in community energy programs and educational initiatives.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0070.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.082
GPT teacher head0.345
Teacher spread0.262 · 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 designQualitative
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

Citations1
Published2025
Admission routes3
Has abstractyes

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