Variation in nasal–obstruent clusters and its influence on <scp>price</scp> and <scp>mouth</scp> in Scouse
Bibliographic record
Abstract
This article has two main goals: (i) to show how nasal–obstruent clusters interact with a Canadian-Raising-type pattern in Liverpool English and (ii) to provide evidence that fine phonetic variation in the realisation of nasal–obstruent clusters influences the production of the preceding vowels. I present quantitative evidence from an acoustic study on price and mouth vowel realisations before nasal–obstruent clusters in Liverpool English. The investigation looks at price and mouth separately before obstruents, nasals and nasal–obstruent clusters, in order to demonstrate that nasal–obstruent clusters influence vowels differently depending on the quality of the vowel. Price realisations before nasal–obstruent clusters are similar to productions before singleton obstruents with the same voicing. Specifically, price has a raised realisation before nasal–voiceless obstruent clusters, but a non-raised realisation before nasal–voiced obstruent clusters, which is the same pattern as before singleton obstruents. Mouth realisations preceding nasal–obstruent clusters show evidence of a greater influence from the nasal. The nucleus formant measurements are similar to those before singleton obstruents, but there is frequent monophthongisation preceding nasal–obstruent clusters in mouth, which is mainly found before singleton nasals. Furthermore, I show that the variation in nasal–obstruent clusters in Liverpool English helps to explain the differences in realisation of the target vowels. Nasal deletion is more frequent in nasal–voiceless obstruent clusters following price, leading to vowel productions similar to those before singleton voiceless obstruents. However, nasal durations are longer in nasal–obstruent clusters following mouth, leading to a greater influence of the nasal in the form of more monophthongal vowel productions.
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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 | 0.001 |
| 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".