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Record W2110534503 · doi:10.2190/lgp5-q4tm-d0u5-w029

Reading, Hearing, and Seeing Poetry Performed

2005· article· en· W2110534503 on OpenAlexaff
Michelle C. Hilscher, Gerald C. Cupchik

Bibliographic record

VenueEmpirical Studies of the Arts · 2005
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPoetryPsychologyNarrativeMetaphorMeaning (existential)Reading (process)Active listeningLiteratureArtLinguisticsCommunicationPhilosophy

Abstract

fetched live from OpenAlex

The study investigated the differences between reading a poem, listening to a poem, and watching a live poetic performance. Stimulus materials included three poems with positive and three with negative themes written by Michelle Hilscher. The 32 participants, including an equal number of males and females in psychology and literature, completed a General Poetry Questionnaire (GPQ) to indicate their experiences and impressions of poetry coming into the study. Following the presentation of each poem, participants answered 14 5-point scale questions in a Poetry Reception Questionnaire (PRQ) which captured cognitive and emotional nuances of poetry reception, and one open-ended question where the participants wrote freely about the poem's meaning. A factor analysis of the GPQ distinguished primarily between participants' responsiveness to the stylistic features and subject matter of poetry. A factor analysis of the PRQ identified global absorption, interpretive engagement, and narrative representation as the primary activities involved in responses to the six poems. A factor analysis of qualitatively derived meaning categories (MC) contrasted an elaboration of style and metaphor as opposed to subject matter and story line. Analyses of variance showed that respondents preferred to read poetry as compared to hearing, or seeing it performed live. By reading poetry themselves, participants were able to explore and interpret the literary devices in poetry in an independent and creative manner, whereas participants who experienced a live performance seemed constrained in their ability to be actively involved in their experience of poetry and therefore found the poetry less engaging.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.150
GPT teacher head0.385
Teacher spread0.235 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations9
Published2005
Admission routes1
Has abstractyes

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