MétaCan
Menu
Back to cohort
Record W2164660261 · doi:10.1080/02699931.2010.515151

Emotion and narrative fiction: Interactive influences before, during, and after reading

2010· review· en· W2164660261 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

VenueCognition & Emotion · 2010
Typereview
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsUniversity of TorontoYork University
Fundersnot available
KeywordsNarrativePsychologyReading (process)SketchAffect (linguistics)MoodCognitive psychologySocial psychologyLiteratureLinguisticsCommunicationArtComputer science

Abstract

fetched live from OpenAlex

Emotions are central to the experience of literary narrative fiction. Affect and mood can influence what book people choose, based partly on whether their goal is to change or maintain their current emotional state. Once having chosen a book, the narrative itself acts to evoke and transform emotions, both directly through the events and characters depicted and through the cueing of emotionally valenced memories. Once evoked by the story, these emotions can in turn influence a person's experience of the narrative. Lastly, emotions experienced during reading may have consequences after closing the covers of a book. This article reviews the current state of empirical research for each of these stages, providing a snapshot of what is known about the interaction between emotions and literary narrative fiction. With this, we can begin to sketch the outlines of what remains to be discovered.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.319
Teacher spread0.274 · 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