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Record W2222925749 · doi:10.1371/journal.pone.0145737

Being in a “Green” Building Elicits “Greener” Recycling, but Not Necessarily “Better” Recycling

2016· article· en· W2222925749 on OpenAlexafffund
David W.–L. Wu, Alessandra DiGiacomo, Peter Lenkic, Vanessa Wong, Alan Kingstone

Bibliographic record

VenuePLoS ONE · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSustainabilityReplicateGarbageObservational studyWork (physics)ReceptacleSustainable designAtmosphericsGreen buildingBusinessEnvironmental scienceArchitectural engineeringEnvironmental economicsEngineeringWaste managementMarketingEcologyEconomicsBiologyMedicine

Abstract

fetched live from OpenAlex

Previous observational work revealed that transient populations in a sustainable building disposed of waste more accurately when compared to patrons in a non-sustainable building. The current study uses an experimental design to replicate this observed effect and to investigate whether or not the built environment influences motivational factors to impact behavior. We find support that a building designed and built to communicate an atmosphere of sustainability can influence waste disposal behavior. Participants in the sustainable building used the garbage receptacle significantly less and compensated by tending to select the containers and organics receptacle more, which actually resulted in more errors overall. Our findings suggest that building atmospherics can motivate people to recycle more. However, atmospherics alone do not appear to be sufficient to elicit the desired performance outcome.

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)0.001
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 score0.127
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.246
Teacher spread0.220 · 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

Citations28
Published2016
Admission routes2
Has abstractyes

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