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Record W2528396400 · doi:10.1016/j.jcps.2016.09.005

How inferred contagion biases dispositional judgments of others

2016· article· en· W2528396400 on OpenAlexafffund
Sean T. Hingston, Justin McManus, Theodore J. Noseworthy

Bibliographic record

VenueJournal of Consumer Psychology · 2016
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsObject (grammar)PsychologySocial psychologyEmotional contagionTask (project management)Cognitive psychologyEconomicsComputer science

Abstract

fetched live from OpenAlex

Abstract Drawing on recent evidence suggesting that beliefs about contagion underlie the market for celebrity‐contaminated objects, the current work investigates how people can make biased dispositional judgments about consumers who own such objects. Results from four experiments indicate that when a consumer comes in contact with a celebrity‐contaminated object and behaves in a manner that is inconsistent with the traits associated with that celebrity, people tend to make more extreme judgments of them. For instance, if the celebrity excels at a particular task, but the target who has come into contact with the celebrity‐contaminated object performs poorly, people reflect more harshly on the target. This occurs because observers implicitly expect that a consumer will behave in a way that is consistent with the traits associated with the source of contamination. Consistent with the law of contagion, these expectations only emerge when contact occurs. Our findings suggest that owning celebrity‐contaminated objects signals information about how one might behave in the future, which consequently has social implications for consumers who own such objects.

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 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 score0.585
Threshold uncertainty score0.329

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 head0.157
GPT teacher head0.343
Teacher spread0.186 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations17
Published2016
Admission routes2
Has abstractyes

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