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Record W2034162610 · doi:10.1002/ejsp.447

The role of amount, cognitive elaboration, and structural consistency of attitude‐relevant knowledge in the formation of attitude certainty

2007· article· en· W2034162610 on OpenAlexafffund
Steven M. Smith, Leandre R. Fabrigar, Bonnie L. MacDougall, Naomi Wiesenthal

Bibliographic record

VenueEuropean Journal of Social Psychology · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsQueen's UniversitySaint Mary's University
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health Research
KeywordsCertaintyElaborationPsychologyConsistency (knowledge bases)AmbivalenceAttitudeSocial psychologyPerceptionCognitionAttitude changeEpistemology

Abstract

fetched live from OpenAlex

Abstract Despite their intuitive plausibility and prominence in theorizing regarding attitude certainty, past studies have provided equivocal evidence for the role of informational and structural consistency factors in perceptions of attitude certainty. The present research attempted to overcome methodological and conceptual limitations in past research in order to establish that amount, cognitive elaboration, and structural consistency of attitude‐relevant knowledge are in fact determinants of attitude certainty. As predicted, certainty was influenced by experimental manipulations of all three constructs. Mediational analyses suggested that the amount and elaboration of information manipulations were mediated by subjective impressions of knowledge. Subjective impressions of amount of thought partially mediated the effects of manipulated elaboration. Finally, perceived ambivalence mediated the effects of manipulated consistency of knowledge. Copyright © 2007 John Wiley & Sons, Ltd.

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)0.006
metaresearch head score (Gemma)0.057
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.057
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.373
Teacher spread0.346 · 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

Citations81
Published2007
Admission routes2
Has abstractyes

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