Bibliographic record
Abstract
This article defends the position that syllables have internal structure, through an examination of s C clusters. Although perceptual factors will be shown to account for why it is sibilants that pattern in unexpected ways in clusters, it will be argued that the behavior of s C clusters cannot be explained solely by functional considerations. Among structural approaches to the syllable, it is argued that s C clusters are best analyzed as coda+onset, not as appendix+onset. The typological patterns of s C cluster well-formedness on the sonority dimension and s C cluster repair are shown to follow only from a coda analysis of s : the patterns follow from constraints on syllable contact. In view of this, it will be shown that the two most commonly defended options for the organization of s as an appendix, the syllable and the prosodic word, can be straightforwardly captured under a coda approach, through a comparative examination of English and Italian. It will further be shown that the distribution of aspiration in English is amenable to a coda analysis of s . Finally, it is argued that some languages require an analysis of s C other than coda+onset. This situation holds in Acoma: an empty nucleus interrupts putative s C clusters in this language.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".