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Record W2791095344 · doi:10.5539/ells.v8n1p1

Emotional Sound Symbolism and the Volta in Shakespearean and Petrarchan Sonnets

2018· article· en· W2791095344 on OpenAlexaffvenue
Cynthia Whissell

Bibliographic record

VenueEnglish Language and Literature Studies · 2018
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsLaurentian University
Fundersnot available
KeywordsSonnetHarshnessPoetryLiteraturePhilosophyArtAcousticsPhysics

Abstract

fetched live from OpenAlex

Sonnets written in the Shakespearean or Petrarchan form are both assumed to present and then answer a problem, but they do so in different ways. The two forms have different rhyming schemes. The volta or turn is predicted to occur between lines 12 and 13 in the first and lines 8 and 9 in the second form. It is argued that sound in poetry is emotionally communicative (symbolic), especially when the predominance of Harsh (e.g., t, r) over Gentle (e.g., l, m) sounds is considered. An analysis of the sounds (phonemes) in various exemplars of the two forms (N=285 sonnets) was undertaken. Shakespeare’s sonnets represented his form and those of a variety of authors represented the Petrarchan form. Predominant Harshness was the dependent variable in a design which compared line and form. There were significant effects associated with line, form, and their interaction (p<.05). Shakespeare’s sonnets, had a lower predominant Harshness than the Petrarchan sonnets. Both Shakespearean and Petrarchan sonnets exhibited a major drop in predominant Harshness (a volta) between lines 8 and 9. Both began on a gentle note, increased in predominant Harshness as problems were being expounded, and returned to a gentler note for their endings. Only Shakespearean sonnets had a spike in harshness in the third quatrain, suggesting that the author was still unfolding problems rather than resolving them there.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.335
Teacher spread0.315 · 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 designQualitative
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

Citations4
Published2018
Admission routes2
Has abstractyes

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