Bibliographic record
Abstract
Gujarati, an Indo-Aryan language, contrasts phonation type in vowels: [baɾ] ‘twelve’ and [ba̤ɾ] ‘outside’ (Pandit, 1957) and the current study looked at Gujarati vowels using acoustic and electroglottographic (EGG) analyses. The participants were native and heritage Gujarati speakers. Heritage speakers were born in Canada or arrived in Canada before seven years of age and learned Gujarati as their first language. Due to limited access to their first language, such as listening more than speaking the language and using Gujarati exclusively at home, it was expected that there might not be a significant difference between the heritage speakers’ breathy and modal vowel productions. This study determined if the acoustic and EGG parameters that differentiate breathy from modal vowels were the same or different for both speaker groups. Some of the parameters used to distinguish phonation type were H1-H2, H1-A1 ('A1' refers to amplitude of the first formant), harmonic-to-noise ratio, and contact quotient. Measurements were made using VoiceSauce (Shue et al., 2009) and EGGWorks (Tehrani, 2009). ANOVA analyses conducted on vowels /a e o/ indicated that fewer parameters distinguish phonation type for heritage than native speakers and the difference between breathy and modal vowels for heritage speakers is smaller in magnitude. The results thus far indicate that heritage speakers acquire a reduced phonation type contrast.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".