Place-based power production deliberations in Saskatchewan: engaging future sustainability
Bibliographic record
Abstract
Abstract This article addresses a gap in knowledge of peoples’ strategies and recommendations concerning power production and achieving reductions in GHGs to address climate change. Employing mixed methods, two-day deliberative focus groups in three communities in Saskatchewan, Canada included pre and post-focus group surveys, coding and analysis of discussions, and the creation of consensus recommendations for sustainable power production in the future. These innovative mixed-methods provide insights into how to advance individual and social learning. Results of comparative case study analysis provide strong support for renewables and illustrate place-based differences. All communities supported renewable sources. The community in proximity to coal, oil, and gas production supported coal, and coal with carbon capture and storage (CCS) and was concerned with the social cost of job loss on the welfare system; engaging the public was not a priority. In contrast, the other two communities stressed the importance of engaging the public and considering all costs, risks, benefits across the entire lifespan of power production sources. To achieve future sustainability, policy implications include addressing important concerns of resource-dependent communities, namely job loss, and conducting holistic policy assessment of potential power production sources that account for carbon and cost across the entire supply chain and include land-use change. Graphical abstract
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".