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Record W3123948877 · doi:10.55016/ojs/sppp.v5i1.42402

Energy Literacy in Canada

2012· article· en· W3123948877 on OpenAlexaffabout
André Turcotte, Michal C. Moore, Jennifer Winter

Bibliographic record

VenueThe School of Public Policy Publications · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsUniversity of CalgaryCarleton University
Fundersnot available
KeywordsWillingness to payContext (archaeology)Energy independencePublic economicsBusinessGovernment (linguistics)Energy policyEnvironmental impact of the energy industryLiteracyNatural resource economicsEnvironmental economicsEconomicsEconomic growthRenewable energyGeographyEngineering

Abstract

fetched live from OpenAlex

Energy plays an important role in everyday activities, whether at a personal, institutional, corporate or social level. In this context, an informed or literate public is critical for the longterm conservation, management, pricing and use of increasingly scarce energy resources. A series of surveys were used to probe the literacy of Canadians with regard to energy issues ranging from relative ranking and importance of energy compared to other national issues, preference for various fuel types and willingness to pay for offsetting environmental impacts from energy generation. In addition, they were asked how Canada’s government should prioritize national energy independence over trade, even if ultimately reducing imports might impact national economic health. The survey revealed that Canadians have a good general knowledge of energy use and relative cost but lack detailed knowledge about sources of energy fuels, as well as sources and linkages with environmental impacts. However, an overwhelming majority of respondents indicated they were concerned about environmental issues; most seemed to direct that concern towards fuels such as coal and nuclear power where support was low compared to a relatively unconcerned view about the often substantial environmental effects of hydro dams or wind farms. Canadians say they have been willing to make adjustments to their own energy-consumption habits, to save money and conserve energy. Further, respondents generally expressed a willingness to pay a surcharge on monthly utility bills, if it would help mitigate the environmental impact of energy generation. There were limits to this view. Support for extra charges falls off rapidly as the costs go up; drivers showed themselves highly resistant to switching their commute to transit, even despite rising gas prices; and respondents were less enthusiastic to the idea of installing home solar panels or switching to electric cars, even when offered a subsidy to do so. In spite of some limitations regarding overall energy literacy, Canadians are also highly skeptical about the information they do get from virtually every stakeholder in the energy arena. In terms of trust and confidence, overall, respondents said they were more willing to listen to academics and economic experts; only a small majority was willing to fully trust those information sources at even low levels. In this serious topic area, respondents indicated they could not trust the credibility of environmental groups, and considered the oil and gas industry and governments by far the least trustworthy sources of information. Finally, in terms of future policy development, most cite the importance of Canada’s energytrading relationship with the United States, but believe it is too dominant, and should diminish, with more effort focused on opening up new export markets elsewhere.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.336

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0060.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0330.002

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 head0.030
GPT teacher head0.322
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2012
Admission routes2
Has abstractyes

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