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Record W2107488010 · doi:10.1086/322896

Affect Monitoring and the Primacy of Feelings in Judgment

2001· article· en· W2107488010 on OpenAlex
Michel Tuan Pham, Joel B. Cohen, John W. Pracejus, G. David Hughes

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 Consumer Research · 2001
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFeelingPsychologyValence (chemistry)Affect (linguistics)Social psychologyContext (archaeology)CognitionCognitive psychologyCommunication

Abstract

fetched live from OpenAlex

Multidisciplinary evidence suggests that people often make evaluative judgments by monitoring their feelings toward the target. This article examines, in the context of moderately complex and consciously accessible stimuli, the judgmental properties of consciously monitored feelings. Results from four studies show that, compared to cold, reason-based assessments of the target, the conscious monitoring of feelings provides judgmental responses that are (a) potentially faster, (b) more stable and consistent across individuals, and importantly (c) more predictive of the number and valence of people's thoughts. These findings help explain why the monitoring of feelings is an often diagnostic pathway to evaluation in judgment and decision making.

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)0.025
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.399
GPT teacher head0.549
Teacher spread0.150 · 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