MétaCan
Menu
Back to cohort
Record W2073157631 · doi:10.1509/jmr.09.0018

From Rumors to Facts, and Facts to Rumors: The Role of Certainty Decay in Consumer Communications

2011· article· en· W2073157631 on OpenAlex
David Dubois, Derek D. Rucker, Zakary L. Tormala

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 Marketing Research · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsCertaintyRumorSalience (neuroscience)SalientAmbiguityInformation transmissionPsychologySocial psychologyAdvertisingBusinessComputer scienceEpistemologyCognitive psychologyPublic relationsPolitical science

Abstract

fetched live from OpenAlex

How does a rumor come to be believed as a fact as it spreads across a chain of consumers? This research proposes that because consumers’ certainty about their beliefs (e.g., attitudes, opinions) is less salient than the beliefs themselves, certainty information is more susceptible to being lost in communication. Consistent with this idea, the current studies reveal that though consumers transmit their core beliefs when they communicate with one another, they often fail to transmit their certainty or uncertainty about those beliefs. Thus, a belief originally associated with high uncertainty (certainty) tends to lose this uncertainty (certainty) across communications. The authors demonstrate that increasing the salience of consumers’ uncertainty/certainty when communicating or receiving information can improve uncertainty/certainty communication, and they investigate the consequences for rumor management and word-of-mouth communications.

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)0.020
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.010
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 science0.0010.000
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 head0.148
GPT teacher head0.423
Teacher spread0.275 · 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