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Record W2166560559 · doi:10.1109/pes.2009.5275904

Creating a culture of conservation in ontario: Approaches, challenges and opportunities

2009· article· en· W2166560559 on OpenAlexaffabout
Steven J. Norrie, Peter E.D. Love

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsToronto and Region Conservation Authority
Fundersnot available
KeywordsRestructuringIncentiveEnergy conservationBusinessWork (physics)ElectricityEnvironmental economicsNatural resource economicsEnvironmental planningEngineeringEconomicsMarket economyFinanceEnvironmental science

Abstract

fetched live from OpenAlex

Ontario has a long history of affordable and reliable electric power that has supported the development of an energy-intensive industrialized society. Electricity sector restructuring, with the breakup of Ontario Hydro in 1998, followed by the re-introduction of central power system planning in 2005, has given Ontario an opportunity to reshape its electricity supply mix with conservation as a priority. However, there are many challenges to transitioning from almost 100 years of supply-side focus to demand-side planning. These challenges include overcoming barriers to resource acquisition through incentives, building capability in the market for delivery and uptake of conservation, and transforming the market so that energy-efficiency becomes the norm. These barriers can be overcome by catalyzing changes in consumer attitudes and behaviours and using incentives and regulations to support lasting cultural change. Although Ontario has made progress in advancing a culture of conservation, much more work needs to be done.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.895

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0260.013
Scholarly communication0.0070.003
Open science0.0020.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.099
GPT teacher head0.235
Teacher spread0.136 · 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 designQualitative
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

Citations1
Published2009
Admission routes2
Has abstractyes

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