The Effects of Local Phonetic Contrasts in Readers' Responses to a Short Story
Bibliographic record
Abstract
The sound of the language in a literary text is often thought to contribute to its meaning. We hypothesize that this is due not to fixed or universal phoneme properties, as theories of phonetic symbolism have supposed, but to the use of local phonetic contrasts to elicit meaning. Writers may set an overall range of phonetic tones that are distinctive to a particular text and then introduce significant variations to achieve local effects. In the present study, an analysis of phoneme distributions developed by Miall (2001) and an approach to phonetic symbolism developed by Whissell (1999, 2000a, 2000b) were applied to a Katherine Mansfield short story. Readers' responses to the story were obtained using Semantic Differential ratings. The findings show the influence of phonetic patterns consistent with the hypothesis that phonemic contrasts elicit local changes in feeling tone. The effects of phonetic symbolism, while evident, were much less pronounced.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".