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Record W2767033874 · doi:10.5539/ells.v7n4p1

Sound Symbolism in Shakespeare’s Sonnets: Evidence of Dramatic Tension in the Interplay of Harsh and Gentle Sounds

2017· article· en· W2767033874 on OpenAlexaffvenue
Cynthia Whissell

Bibliographic record

VenueEnglish Language and Literature Studies · 2017
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsLaurentian University
Fundersnot available
KeywordsSonnetPoetryTone (literature)LiteratureCoupletDominance (genetics)HistoryPsychologyArtLinguisticsAcousticsPhilosophyPhysics

Abstract

fetched live from OpenAlex

This paper addresses the role of meaningful sounds in poetic communication. A sound symbolic system (Whissell, 2000) was employed to score Shakespeare’s 154 sonnets in terms of the percentage of Harsh (e.g., sh, oo, r, k, p) and Gentle (e.g., l, long e, th, eh, m) sounds in each line. Significant differences in the employment of emotional sounds across lines suggest that the structure of the sonnets is affectively dramatic. Four stages unfold across three quatrains and a couplet. These are the establishment of the problem (lines 1-4; excess of Harsh sounds), its enlargement (lines 5-8; greater dominance of Harsh sounds), multiple emotional reversals (lines 9-12; alternating ascendance of Gentle and Harsh sounds), and a closing coda (lines 13, 14; an echo of the sounds in the first quatrain). In the order of their publication, which has been touted as potentially autobiographical, the sonnets provide a picture of repeated swings between Gentle and Harsh emotional extremes. Individual sonnets critically recognized for their distinctive emotional tone display the appropriate preponderance of Gentle or Harsh sounds.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score0.261

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 head0.041
GPT teacher head0.397
Teacher spread0.356 · 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 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
Published2017
Admission routes2
Has abstractyes

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