Bibliographic record
Abstract
Studies on Mandarin tone sandhi have focused on Beijing Mandarin. Taiwan has been politically separated from mainland China since 1949, but it is not known if tone sandhi in Taiwan Mandarin displays different patterns or characteristics. However, there has been no comparative study to investigate if Beijing Mandarin and Taiwan Mandarin display the same tone sandhi pattern. This study aims to fill this gap by comparing Beijing and Taiwan Mandarin through a productive experiment to examine acoustic differences between sandhied tone 3 and lexical tone 2. The results indicate that tone 3 sandhi among Mandarin dialects is not a homogeneous category, but displays a graded phenomenon of a categorical change and tonal reduction. The experimental evidence shows that acoustic difference between sandhied tone 3 and lexical tone 2 is larger in Beijing Mandarin than that in Taiwan Mandarin. Gender effects are also detected and acoustic difference in female samples is consistently larger than that in male samples across Beijing and Taiwan Mandarin. The findings suggest that the third tone sandhi in Beijing Mandarin is more like a categorical change (i.e., changed to lexical tone 2) whereas the sandhi in Taiwan Mandarin is more like a tonal reduction.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".