A comparison of the Cantonese pronunciations recorded in Langwen chujie zhongwen cidian and Zhonghua xin zidian = "Langwen chu jie Zhong wen ci dian" yu "Zhonghua xin zi dian" yue yu zhu yin bi jiao yan jiu
Bibliographic record
Abstract
Cantonese, the standard and socially the most prestigious of the Yue dialects, is certainly an important dialect for investigation. Apart from being a regional dialect of the Guangdong province, Hong Kong and Macau, it is also widely used throughout the Chinese communities in the U.S., Canada, Australia and the U.K. In the past seventy years, a number of dictionaries on Cantonese have been compiled. Among them, the Langwen chujie zhongwen cidian 朗文初階中文詞典 and the Zhonghua xin zidian中華新字典 are both widely used and are therefore influential standards on the dialect. Yet, despite their importance, specialist and systematic studies on the Cantonese pronunciations given in them are few and far between. The present dissertation is the first attempt to study the subject. \n\n\n\nIt is found that many Chinese characters are given surprisingly different pronunciations in the two dictionaries. However, the two dictionaries do not state very clearly how they determine the Cantonese pronunciation given in them. This results in great confusion for dictionary users. The present thesis attempts to analyze differences between the two dictionaries with reference to (1) the number of pronunciations given to each character, (2) the order of listing the pronunciations, (3) tone change and (4) the listing of commonly used pronunciations.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".