Bibliographic record
Abstract
Vowels are longer before sonorants and voiced obstruents than before voiceless obstruents. This pattern is found in many languages and by some is argued to be universal. In some languages it has been phonologized and gives rise to alternations. Three cases are examined: Western Slavic, English and German. In all cases, I argue that the mechanism which modifies vowel duration in a voiced context is phonetic in kind (not phonological), and involves voice-induced lengthening, rather than so-called ‘pre-fortis clipping’. Phonetic length can be phonologized by its inscription into the lexical recording of morphemes. Phonological processes such as (Canadian) raising in English or oo > uu raising in Western Slavic may then take this lexical length as an input. This analysis allows us to keep spontaneous and non-spontaneous voicing truly separate: voicing in sonorants and vowels is never phonologically active, its spreading can only occur in the phonetics (‘passive voicing’ in Laryngeal Realism). A strong argument in favour of this view is the fact that cross-linguistically sonorants appear to always be among the triggers of voice-induced vowel lengthening: there are no cases where vowels lengthen before voiced obstruents, but not before sonorants. This is predicted if lengthening is phonetic, but unexpected if it were phonological: the phonologically active voicing of obstruents should at least sometimes be the only trigger.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".