Mapping the portrayal of small modular reactors in Canadian Energy Solutions
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".