The theory of adaptive dispersion and acoustic-phonetic properties of cross-language lexical-tone systems.
Bibliographic record
Abstract
Lexical-tone languages use fundamental frequency (F0) to convey word-meaning. Nearly half of all languages use lexical tone [Maddieson (2008)], yet those systems are under-studied. To increase our understanding of speech-sound inventory organization, I extend to tone-systems a model of vowel-system organization, the theory of adaptive dispersion (TAD) [Liljencrants and Lindblom (1972)]. This is a cross-language investigation of whether and how tone-inventory size affects acoustic tone-space size. Five languages with different-sized tone-inventories were compared: Cantonese (six tones), Thai (five tones), Mandarin (four tones), Yoruba (three tones), and Igbo (two tones). Six native speakers (three female) of each language produced 18 CV syllables in isolation, with each of his/her language’s tones, six times. Tonal F0 (semitones) was measured at three equidistant points across the vowel. Each language’s tone-space was defined in two ways: (1) the F0 difference between its highest and lowest tones and (2) the configuration of tones in a 2-D space defined by onset F0 x offglide F0. Following the TAD, I predicted that languages with larger tone-inventories would have larger tone-spaces; this was not supported by (1). However, the dispersion of tones in (2) supports the TAD hypothesis that sound-categories will be well-dispersed across the space and highly contrastive.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".