Categorization of a <i>s</i> <i>i</i> <i>b</i> <i>i</i> <i>l</i> <i>a</i> <i>n</i> <i>t</i>+<i>v</i> <i>o</i> <i>w</i> <i>e</i> <i>l</i> continuum in Japanese, Mandarin, and English
Bibliographic record
Abstract
A continuum of 168 synthetic sibilant+vowel stimuli was categorized by listeners of three languages (Japanese, Mandarin, and English). The contoid portion of each stimulus consisted of a 190-ms high-pass filtered white noise whose cutoff frequency varied from 1800 to 3800 Hz in 7 steps. (Signals were low-pass filtered at 8500 Hz before resampling to 22.05 kHz for playback.) Each (cascade formant) vocoid portion was 210 ms in duration varying in three F1 levels (from 300 to 430 Hz) crossed with four F2 levels (from 850 to 2100 Hz, with fixed F3 through F5). Each of the 84 noise+vocoid patterns was combined with two different F2 transition patterns, designed to slightly favor more alveolar or more palatal fricative responses. Appropriate response sets were determined in pilot studies for each language by native speakers on the research team. Corresponding software was designed to allow computer-controlled categorization. Data collection for 9 Japanese, 20 Mandarin, and 4 English listeners is complete (more is planned). Responses to the stimuli spanned at two or three sibilant and several (high-to-mid) vowel categories in each language. Possible effects of language-specific phonotactics on the response patterns will be discussed.
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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 | 0.000 |
| 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) | 0.002 | 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".