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Record W4388865238 · doi:10.1080/2154896x.2023.2274264

Mapping the portrayal of small modular reactors in Canadian Energy Solutions

2023· article· en· W4388865238 on OpenAlexfundaboutno aff
Alexandra Middleton

Bibliographic record

VenueThe Polar Journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
FundersArcticNet
KeywordsContext (archaeology)IndigenousArcticRenewable energyViewpointsEnergy transitionEnergy supplyEnvironmental resource managementEnvironmental planningEnvironmental economicsBusinessPolitical scienceGeographyEngineeringEnergy (signal processing)Environmental scienceEconomics

Abstract

fetched live from OpenAlex

The Arctic region exhibits socio-economic disparities and diverse development strategies among Arctic states.Energy solutions in the Arctic necessitate industrialisation and ground infrastructure, with many offgrid Arctic communities relying on diesel power due to limited accessibility and high transportation costs.Moreover, there is growing interest in renewable and low carbon energy, followed by the consideration of nuclear solutions as part of the transition to achieving net-zero emissions.Local perspectives are crucial in formulating sustainable energy policies tailored to specific needs.Canada, with its established nuclear supply chain and technical capabilities, provides a pertinent case study for incorporating Small Modular Reactors (SMRs) as part of its energy transition.Within the context of transitions in Arctic communities, this paper's primary focus is on mapping the portrayal of SMRs within Canada's energy solutions.This investigation relies on publicly available sources to analyse how SMRs are depicted and integrated into Canada's energy landscape.This paper analyzes publicly available discourse to examine various perspectives on SMRs, emphasising the most prominent viewpoints, including Indigenous perspectives.The results provide an exploration of the intricate difficulties and potential benefits associated with SMRs in the context of Canada's shift towards cleaner energy sources.Indigenous viewpoints add a wide range of perspectives, both endorsing and opposing SMRs, highlighting the complexity of SMRs employment and the need for future research on this emerging topic.

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)0.002
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 score0.498
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
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 head0.073
GPT teacher head0.286
Teacher spread0.214 · 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

Citations7
Published2023
Admission routes2
Has abstractyes

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