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Record W3202195339

The Language of Quotations in Hong Kong Chinese Newspapers

2014· article· en· W3202195339 on OpenAlexaff
Anthony C. Lister

Bibliographic record

VenuePapers from the Annual Meetings of the Atlantic Provinces Linguistic Association (PAMAPLA) · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsNewspaperMandarin ChineseHistoryPopulationChinese languageStandard ChineseLinguisticsAdvertisingPsychologyDemographySociologyBusinessPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

In Hong Kong standard written Chinese is Mandarin while Cantonese, which is the language of the vast majority of the territory’s population and is entirely acceptable when spoken, has very low status in its written form. This poses a problem for newspaper editors when quoting Cantonese speech. In the past, it was translated into Mandarin, but in recent years there has been an increasing use of Cantonese. This article examines the reporting of a speech in two different newspapers, the quality Ming Pao and the mass circulation Apple Daily, to study whether there was variation in the amount of Cantonese in the quotations. It was found that there was slightly less in Ming Pao than in Apple Daily, though not as much as might have been expected. There was also a greater use of quotation in Ming Pao Pao, which again was not expected based on earlier research. However, comparison with the actual words as recorded in a YouTube video revealed that both newspapers still reduced the amount of Cantonese.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0020.003
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.005
GPT teacher head0.250
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2014
Admission routes1
Has abstractyes

Explore more

Same venuePapers from the Annual Meetings of the Atlantic Provinces Linguistic Association (PAMAPLA)Same topicLinguistic Variation and MorphologyFrench-language works237,207