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Record W1494559763

Attitudes towards new renewable energy technologies in the eastern Ontario highlands.

2012· article· en· W1494559763 on OpenAlexaffvenueabout
Stewart Fast, Robert McLeman

Bibliographic record

VenueJournal of rural and community development · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsNIMBYRenewable energyHydroelectricityFeed-in tariffFocus groupBusinessWind powerRural areaGovernment (linguistics)Agricultural economicsEnvironmental protectionEconomic growthGeographySocioeconomicsNatural resource economicsEnergy policyMarketingPolitical scienceEconomicsEngineeringCivil engineering
DOInot available

Abstract

fetched live from OpenAlex

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

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.491
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.039
GPT teacher head0.282
Teacher spread0.244 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations22
Published2012
Admission routes3
Has abstractyes

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