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Record W2064899973 · doi:10.1371/journal.pone.0053856

A Sustainable Building Promotes Pro-Environmental Behavior: An Observational Study on Food Disposal

2013· article· en· W2064899973 on OpenAlexafffund
David W.–L. Wu, Alessandra DiGiacomo, Alan Kingstone

Bibliographic record

VenuePLoS ONE · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSustainabilityPsychological interventionGarbageEnvironmental psychologyEmpirical researchObservational studySustainable livingOrder (exchange)PsychologyBusinessEnvironmental resource managementSocial psychologyEngineeringEcologyEconomicsMedicine

Abstract

fetched live from OpenAlex

In order to develop a more sustainable society, the wider public will need to increase engagement in pro-environmental behaviors. Psychological research on pro-environmental behaviors has thus far focused on identifying individual factors that promote such behavior, designing interventions based on these factors, and evaluating these interventions. Contextual factors that may also influence behavior at an aggregate level have been largely ignored. In the current study, we test a novel hypothesis--whether simply being in a sustainable building can elicit environmentally sustainable behavior. We find support for our hypothesis: people are significantly more likely to correctly choose the proper disposal bin (garbage, compost, recycling) in a building designed with sustainability in mind compared to a building that was not. Questionnaires reveal that these results are not due to self-selection biases. Our study provides empirical support that one's surroundings can have a profound and positive impact on behavior. It also suggests the opportunity for a new line of research that bridges psychology, design, and policy-making in an attempt to understand how the human environment can be designed and used as a subtle yet powerful tool to encourage and achieve aggregate pro-environmental behavior.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.076
GPT teacher head0.280
Teacher spread0.203 · 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.

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

Citations60
Published2013
Admission routes2
Has abstractyes

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