Canadian Journal of Linguistics/Revue ca.nadienne de Linguistique 35(4):331-349 Dec.!d~c. 1990 Prelinked and Floating Glottal Stops In Fuzhou Chinese
Bibliographic record
Abstract
Numerous interesting problems in the phonology of different dialects of Chi nese tend to be buried in Chinese-language sources, or have not yet gained the attention of phonologists in genera!.1 One such case is the final glottal stop in modern Fuzhou, with respect to its behaviour synchronically and its historical origins. The final glottal stop came from two earlier sources, *-k and *-r. While *-k has completely merged with *-r in stressed syllables, evidences of the earlier contrast can still be found in the modern dialect in how it behaves in more weakly stressed syllables in tone sandhi spans, and in its effect on adjacent consonants. It is proposed here that the con tinued relevance of the former phonological contrast can be accounted for by treating the final glottal stop from *-k as a prelinked glottal stop, and the one from *-r as a floating segment within the autosegmental approach. In this paper I will trace the history of these two codas, as well as address the implications that the differences in representation have with respect to subsequent changes in the language. I will conclude with a discussion of
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.168 | 0.048 |
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".