Bibliographic record
Abstract
A large body of evidence has shown that relative change in spectral pattern possesses some invariant properties for vowels across speakers in /hVd/ environment. While formant frequency plots from vowel steady-states result in categories with large overlap, better separation of vowel categories is obtained using onset and offset formant frequencies [e.g., Hillenbrand et al., J. Acoust. Soc. Am. 97, 3099–3111 (1995)]. To a large extent, this invariance was also shown in other /CVC/ contexts using all combinations of /h,b,d,g,p,t,k/ [Hillenbrand et al., J. Acoust. Soc. Am. 109, 748–763 (2001)]. The present study explored spectral change in vowels produced in environments where larger differences might be expected across contexts. Nineteen men and 39 women were asked to produce fourteen Canadian English vowels in /hV/, /hVd/, /hVt/, /hVl/, /hVr/, /hVnd/, /hVg/, and /dVd/. Subjects read standard English orthographic representations of the target words which were embedded both in a sentence which indicated the rhyme of the target word—e.g., ‘‘Swooned rhymes with hoond’’ as well as in a sentence which indicated the pronunciation of onset and vowel—e.g., ‘‘Hood sounds like hoog.’’ Formant frequencies were measured and tracked for each token and differences across consonant contexts were analyzed. [Work supported by SSHRC.]
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".