Systematic co-variation of monophthongs across speakers of New Zealand English
Bibliographic record
Abstract
The study of phonetic variation and change has tended to concentrate on particular variables in isolation, and it has proven challenging to move beyond an analysis of individual variables or small groups of variables, towards a better theoretical and empirical understanding of entire vowel systems. We develop a methodology that facilitates the study of co-variation, and introduce a large scale analysis of how elements of full sound systems co-vary across hundreds of speakers, demonstrating how constellations of vocalic variables operate together. Our data-set comprises F1 and F2 for 10 monophthongs of New Zealand English. We first obtain estimates of how advanced each speaker is with respect to changes in each of the vowels, irrespective of known predictors of sound change (i.e. year of birth, gender, speech rate). This is done by extracting by-speaker intercepts from Generalised Additive Models. We then use Principal Component Analysis on these intercepts to investigate the underlying structural co-variation that exists across the vocalic variables. Within a large subset of vowels, we see ‘leaders’ and ‘laggers’ of sound change; however, there are also groups of vowels which stand in opposition to each other, such that if a speaker is innovative in one, they tend to be conservative in the other. Some sets of covarying vowels could be linked by structural relationships (such as chain-shifting), but there are also covarying sets of vowels with no clear structural relationship, and which may be linked by shared social meaning. Our analysis provides novel insights into the structure of sound systems, demonstrating the existence of structured patterns in the realisations of specific vocalic variables across a large group of speakers. This approach offer a means to overcome long-standing methodological challenges in the study of phonetic co-variation, paving the way for research to move beyond the analysis of individual variables, towards an understanding of variation and co-variation in sound systems.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".