Structured heterogeneity in Scottish stops over the twentieth century
Bibliographic record
Abstract
How and why speakers differ in the phonetic implementation of phonological contrasts, and the relationship of this ‘structured heterogeneity’ to language change, has been a key focus over 50 years of variationist sociolinguistics. In phonetics, interest has recently grown in uncovering ‘structured variability’—how speakers can differ greatly in phonetic realization in non-random ways—as part of the longstanding goal of understanding variability in speech. The English stop voicing contrast, which combines extensive phonetic variability with phonological stability, provides an ideal setting for an approach to understanding structured variation in the sounds of a community’s language which illuminates both synchrony and diachrony. This paper examines the voicing contrast in a vernacular dialect (Glasgow Scots) in spontaneous speech, focusing on individual speaker variability within and across cues, including over time. Speakers differ greatly in the use of each of three phonetic cues to the contrast, while reliably using each one to differentiate voiced and voiceless stops. Interspeaker variability is highly structured: speakers lie along a continuum of use of each cue, as well as correlated use of two cues—VOT and closure voicing—along a single axis. Diachronic change occurs along this axis, towards a more aspiration-based and less voicing-based phonetic realization of the contrast, suggesting an important connection between synchronic and diachronic speaker variation.
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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 | 0.002 |
| 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 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".