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Record W974540706 · doi:10.5353/th_b4702002

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

2011· dissertation· en· W974540706 on OpenAlexaboutno aff
Ngai-ling Chan

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicEducational Reforms and Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsArt

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.023
GPT teacher head0.293
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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