Bibliographic record
Abstract
This work examines the influence of native phonation contrast type on the production of English stops in different phonetic contexts/reading styles. Proficient English speakers from four different L1 language backgrounds produced words in three different contexts: words in isolation, phrase-final in a carrier sentence, and in a reading passage. Language backgrounds were representative of three types of phonation contrasts: Mandarin (aspiration contrast: [pʰ ~ p]), Tagalog (voicing contrast: [p ~ b]), and Urdu (4-way phonation contrast [pʰ ~ p ~ bʰ ~ b]). 11 participants from each group were compared with a control group of L1 English speakers. Aspiration and closure voicing were measured. All groups produced English voiceless stops as aspirated; however, there was considerable variation in the voiced stops, with Mandarin speakers producing less voicing, and Urdu and Tagalog speakers producing more voicing, than English speakers across speech styles, showing an asymmetrical influence of L1 phonation on production of the English contrast, in line with other recent work.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".