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Record W2162171543 · doi:10.1177/1754073913489749

Emotional States from Affective Dynamics

2013· article· en· W2162171543 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

VenueEmotion Review · 2013
Typearticle
Languageen
FieldPsychology
TopicEmotions and Moral Behavior
Canadian institutionsConcordia UniversityUniversity of Toronto
Fundersnot available
KeywordsPsychologyCognitive psychologyValence (chemistry)Affect (linguistics)Dynamics (music)Social psychologyCommunication

Abstract

fetched live from OpenAlex

Psychological constructivist models of emotion propose that emotions arise from the combinations of multiple processes, many of which are not emotion specific. These models attempt to describe both the homogeneity of instances of an emotional “kind” (why are fears similar?) and the heterogeneity of instances (why are different fears quite different?). In this article, we review the iterative reprocessing model of affect, and suggest that emotions, at least in part, arise from the processing of dynamical unfolding representations of valence across time. Critical to this model is the hypothesis that affective trajectories—over time—provide important information that helps build emotional states.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.940
Threshold uncertainty score0.989

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)0.0530.012

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 head0.024
GPT teacher head0.329
Teacher spread0.305 · 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