Attitudes towards new renewable energy technologies in the eastern Ontario highlands.
Bibliographic record
Abstract
As governments seek to expand generation capacity from sources such as solar farms, wind turbines, hydroelectric and biomass generators, rural responses to renewable energy become increasingly important. In early 2011 we conducted a mail-out survey of permanent residents, a concurrent internet-based survey of seasonal residents and follow-up focus groups in two rural eastern Ontario municipalities to assess public attitudes and to project acceptance and potential uptake of various technologies. Survey participation was relatively high (n = 180, response rate 22%). One focus group included local and regional government decision-makers, the other for residents representing a range of socio-economic and demographic groups. Results showed strong support among residents to pursue alternative energy sources (89%), mostly out of concerns with rising energy costs, but also from a desire to use local energy sources. Support was highest for solar technologies (87%) and lowest for wind turbines (58%) and new hydroelectric dams (58%). There was little evidence of NIMBY views being prevalent among permanent residents. Seasonal cottage dwellers were less supportive of hydroelectric dams and a wood pellet facility. Our findings suggest rural residents start with favourable attitudes towards alternative forms of energy production. Acceptance and uptake will likely be strengthened by locally relevant demonstration projects and by supporting citizen involvement in task groups, workshops or other venues for information sharing. Keywords: Renewable energy, attitudes, NIMBY, acceptance, feed-in-tariff 1.0 Introduction
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".