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Record W3004593468 · doi:10.1075/ssol.18009.gib

What psycholinguistic studies ignore about literary experience

2019· article· en· W3004593468 on OpenAlexaff
Raymond W. Gibbs, Herbert L. Colston

Bibliographic record

VenueScientific Study of Literature · 2019
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsycholinguisticsEmbodied cognitionReading (process)ComprehensionReading comprehensionPsychologyLinguisticsCognitionFocus (optics)Emphasis (telecommunications)Cognitive psychologyCognitive scienceComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

Abstract Multiple decades of psycholinguistic research exploring people’s reading of different types of language has delivered much improved understanding of textual comprehension experience. Psycholinguistic studies have typically focused on a few cognitive and linguistic processes presumed to be central in reading comprehension of language, but this emphasis has omitted other processes and products readers commonly experience in their imaginative, aesthetic encounters with literature. Our paper describes some of the limitations of psycholinguistics for explaining people’s literary experiences. Nonetheless, we argue that recent research on embodied simulation processes may help close the gap between psycholinguistics, with its emphasis on generic processes of non-literary language use, and studies associated with the scientific study of literature with their focus on phenomenological, lived reactions to literary texts.

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)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.002
Science and technology studies0.0040.025
Scholarly communication0.0130.018
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.033
GPT teacher head0.366
Teacher spread0.333 · 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 designTheoretical or conceptual
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

Citations11
Published2019
Admission routes1
Has abstractyes

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