Phonoemotional Profiling: A Description of the Emotional Flavour of English Texts on the Basis of the Phonemes Employed in Them
Bibliographic record
Abstract
Research employing three large lists of words rated along emotional dimensions (total N = 15,761 words) supported a prior claim that most phonemes have a distinct emotional character. Different phonemes tended to occur more often in different types of emotional words. When phonemes were grouped along eight radii in a two dimensional emotional space defined by Pleasantness and Activation (Pleasantness, Cheeriness, Activation, Nastiness, Unpleasantness, Sadness, Passivity, and Softness), it became possible to draw profiles of texts in terms of their preferential use of different classes of phonemes. Four experiments were performed to illustrate the manner in which phonemes in nonsense words are related to emotion, and evidence of the validity of character assignments was investigated and received support in three further analyses. The emotionality of phonemes was related to both place and manner of articulation and to properties of the auditory signal itself. Phonoemotional profiles were drawn for several types of material and provided supporting evidence for the validity of the assignment of emotional character to phonemes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".