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Record W2692863388 · doi:10.1177/0013916517715296

Psychological Barriers to Energy Conservation Behavior: The Role of Worldviews and Climate Change Risk Perception

2017· article· en· W2692863388 on OpenAlexaff
Karine Lacroix, Robert Gifford

Bibliographic record

VenueEnvironment and Behavior · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsClimate changePerceptionRisk perceptionPsychologySocial psychologyEnergy conservationEnergy (signal processing)Ecology

Abstract

fetched live from OpenAlex

We proposed and tested a conceptual model of how cultural cognition worldviews, climate change risk perception, and psychological barriers are related to reported energy conservation behavior frequency. Egalitarian and communitarian worldviews were correlated with heightened climate change risk perception, and egalitarian worldviews were correlated with weaker perceived barriers to reported energy conservation behavior. Heightened climate change risk perception was, in turn, associated with fewer perceived barriers to engagement in energy conservation behavior and more reported energy conservation behaviors. The relation between cultural worldviews and perceived barriers was partly mediated by climate change risk perception. Individuals with distinct worldviews perceived psychological barriers differently, and some barrier components were more strongly related to energy conservation behavior than others. Overall, climate change risk perception was the strongest predictor of perceived barriers and of energy conservation behavior frequency. Future efforts should focus on reducing the psychological barriers to energy conservation behavior identified in this study.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.005
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.254
GPT teacher head0.410
Teacher spread0.156 · 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".

Quick stats

Citations166
Published2017
Admission routes1
Has abstractyes

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