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Record W4396585109 · doi:10.54175/hsustain3020012

Sustainable Development and Underexplored Topics in Canada’s Energy Transition

2024· article· en· W4396585109 on OpenAlexaffabout
Michael Benson

Bibliographic record

VenueHighlights of Sustainability · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsRoyal Roads University
Fundersnot available
KeywordsSustainabilityEnergy (signal processing)Energy transitionIndigenousVariety (cybernetics)Political scienceFocus groupSustainable energyTransition (genetics)Public relationsEnvironmental resource managementBusinessMedicineEngineeringRenewable energyComputer scienceEconomicsMarketingEcology

Abstract

fetched live from OpenAlex

Canada’s energy system is undergoing a fundamental shift, which will change how Canadians produce and consume energy. The success of Canada’s energy transition will be influenced by the ability of energy practitioners to manage the tensions and trade-offs in a variety of topics. The purpose of this research was to identify topics that are relevant to Canada’s energy transition and to identify the concepts that energy practitioners are using to manage the tensions and trade-offs in these topics. According to in-depth interviews with Canadian energy practitioners in 2021, the two most important topics in Canada’s energy transition are climate change and reconciliation with Indigenous Peoples. In addition, according to a 2021 focus group with Canadian energy practitioners, three relevant and underexplored topics in Canada’s energy transition are environmental rights, a systemic reduction in energy consumption, and learning from the energy transition in other countries, notably, Germany. These three underexplored topics were studied by completing additional in-depth interviews in 2022 and 2023, and a causal loop analysis in 2023. This research suggests that the concepts of sustainable development and multi-level perspective are complementary, can increase understanding of important and underexplored energy transition topics, and can generate solutions to complex sustainability challenges.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.829
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.244
Teacher spread0.233 · 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 designTheoretical or conceptual
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
Published2024
Admission routes2
Has abstractyes

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