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Record W2558850584 · doi:10.1080/1533015x.2016.1241166

Developing interventions to change recycling behaviors: A case study of applying behavioral science

2016· article· en· W2558850584 on OpenAlexaff
Heather L. Gainforth, Kate Sheals, Lou Atkins, Richard Jackson, Susan Michie

Bibliographic record

VenueApplied Environmental Education & Communication · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersUniversity College London
KeywordsPsychological interventionContext (archaeology)Intervention (counseling)PsychologyBehavior changeApplied psychologySocial psychology

Abstract

fetched live from OpenAlex

The Theoretical Domains Framework (TDF) and the Behavior Change Wheel (BCW) are frameworks that can be used to develop recycling interventions. The aim of this study was to demonstrate the utility of these frameworks for developing recycling interventions. 20 semistructured interviews with university building users were analyzed using the TDF and BCW. Environmental context and resources, beliefs about consequences, knowledge, and intention were identified as the key theoretical domains influencing recycling behaviors. The BCW was used to develop recommendations for intervention. This research is the first case study to demonstrate how the TDF and the BCW can be used to develop recycling interventions.

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)0.029
metaresearch head score (Gemma)0.036
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0080.004
Scholarly communication0.0030.003
Open science0.0040.006
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.379
Teacher spread0.303 · 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

Citations75
Published2016
Admission routes1
Has abstractyes

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