Transformative power production futures: citizen jury deliberations in Saskatchewan, Canada
Bibliographic record
Abstract
Abstract Background Transforming power production systems to achieve net zero emissions and address climate change will require deep structural changes, partially dependent on community perceptions of the necessary energy transition. The article presents results from 2-day citizen juries held in four communities of Saskatchewan, Canada: Estevan, Swift Current, Regina, and Saskatoon in 2021/22 whose purpose was to determine if place attachment impacts future power production preferences and whether social learning can be achieved. Mixed research methods included a survey before and after the citizen juries and a qualitative analysis of the discussions and outputs. Results Research results confirm that while there are common concerns across communities about unbiased information, transparent decision-making, justice/equity concerns, and people's involvement, community-imagined energy futures can be very divergent. Not only place-based attachment, the existent industry and infrastructure surrounding the community impact preferences but also openness to learning and group dynamics contribute. Focused deliberations on the complex problem of climate change advance social learning. The coal, oil, and gas community of Estevan supported coal, natural gas, and carbon capture and sequestration (CCS) to a substantially larger extent than other communities, even increasing their preference for coal after the citizen jury. Saskatoon chose Small Modular Reactors (SMR) as their top choice, whereas Swift Current switched from preferring natural gas to solar and SMRs. Conclusions The findings from the jury sessions suggest changing attitudes toward SMRs as a potential source of energy, as well as a shift from cost considerations to environmental. Future research implications could include differing methodologies and potentially partnering beyond academia. Jurors all expressed the desire for greater government leadership, urging the government to demonstrate accountability, hold large enterprises accountable, and be more proactive in bringing parties together.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.042 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".