Bibliographic record
Abstract
Quantal effects signify nonlinear relations in the properties of speech sounds, traditionally emphasizing articulatory-acoustic relations [Stevens, 1989, JPhon 17, 3-45]; these relations hold important clues to how continuous phonetic parameters map onto discrete phonological categories. Stevens described quantal states in laryngeal speech function, showing how the vocal fold abduction-adduction continuum can be partitioned into breathy, modal, and pressed phonatory quanta. This account, however, relies on a one-dimensional conceptualization of the larynx, which recent developments in laryngeal phonetic theory reveal to be inadequate [Edmondson & Esling, 2006, Phonology 23, 157-191]: a more realistic model must include the epilarynx. We reopen the issue of quantal laryngeal speech behavior in the context of recent research demonstrating quantal biomechanical properties in labial articulation [Gick, Stavness, Chiu, & Fels, 2011, Can. Acoust. 39, 178-179]. Our "whole larynx" approach [Moisik & Esling, 2011, ICPhS, 1406-1409] countenances epilaryngeal influence on laryngeal articulatory and phonatory possibilities through quantal biomechanical and aero-mechanical effects. We demonstrate, through computer simulation, three novel cases of laryngeal quantality: (1) vocal-ventricular interaction in glottal stop, glottalization, and laryngealization; (2) aryepiglotto-epiglottal stricture in pharyngeal consonants; (3) epilaryngeal predisposition for growl-like or harsh phonation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".