MétaCan
Menu
Back to cohort
Record W2277252510 · doi:10.1177/0022427815625093

An Experimental Test of Deviant Modeling

2016· article· en· W2277252510 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

VenueJournal of Research in Crime and Delinquency · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsSimon Fraser UniversityUniversity of Waterloo
Fundersnot available
KeywordsPsychologyTest (biology)Social psychologyBehavioral modelingControl (management)Computer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Objectives: Test the effect of deviant peer modeling on theft as conditioned by verbal support for theft and number of deviant models. Methods: Two related randomized experiments in which participants were given a chance to steal a gift card (ostensibly worth CAN$15) from the table in front of them. Each experiment had a control group, a verbal prompting group in which confederate(s) endorsed stealing, a behavioral modeling group in which confederate(s) committed theft, and a verbal prompting plus behavioral modeling group in which confederate(s) did both. The first experiment used one confederate; the second experiment used two. The pooled sample consisted of 335 undergraduate students. Results: Participants in the verbal prompting plus behavioral modeling group were most likely to steal followed by the behavioral modeling group. Interestingly, behavioral modeling was only influential when two confederates were present. There were no thefts in either the control or verbal prompting groups regardless of the number of confederates. Conclusions: Behavioral modeling appears to be the key mechanism, though verbal support can strengthen the effect of behavioral modeling.

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)0.003
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.306
GPT teacher head0.548
Teacher spread0.242 · 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