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Record W2012041761 · doi:10.1177/0013916513520416

The Influence of Descriptive Social Norm Information on Sustainable Transportation Behavior

2014· article· en· W2012041761 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnvironment and Behavior · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDescriptive statisticsBaseline (sea)Norm (philosophy)Descriptive researchSustainable transportControl (management)PsychologyTransport engineeringSocial psychologyBusinessSustainabilityEngineeringPolitical scienceComputer scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

A month-long field experiment evaluated the impact of descriptive social norm information on self-reported reduction of private vehicle use. Following a baseline week, participants were asked to reduce their vehicle use by 25% and were randomly assigned to a control condition or to a low or high social norm condition in which they received information that either under- or over-reported others’ successful efforts to switch to sustainable transportation. Results indicated a significant linear trend, such that messages highlighting more prevalent descriptive social norms increased sustainable transportation behavior (relative to private vehicle use) for commuting, but not non-commuting, purposes. Participants in the high social norm condition decreased their commuting-related private vehicle use by approximately five times, compared with baseline. Car-use message campaigns can reduce private vehicle use by highlighting descriptive norms about others’ sustainable transportation efforts, but these messages appear to be most effective for commuting 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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.414

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.212
Teacher spread0.207 · 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