Phonoemotional Profiling: A Description of the Emotional Flavour of English Texts on the Basis of the Phonemes Employed in Them
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it