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Record W2004663469 · doi:10.5539/ies.v1n4p44

Challenges of Chinese Language Education in Multi-lingual Societies: Hong Kong and Singapore

2008· article· en· W2004663469 on OpenAlexvenueno aff
Ho Kin Tong, Yeng-Seng Goh

Bibliographic record

VenueInternational Education Studies · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsChinaMedium of instructionFirst languageLearner autonomyChinese educationReading (process)Chinese languagePoliticsMandarin ChineseStandard ChinesePolitical scienceOfficial languageMultilingual EducationEconomic growthSociologyLanguage educationPedagogyLinguisticsLaw

Abstract

fetched live from OpenAlex

This paper aims to study the current challenges of Chinese language education in the multilingual societies of Hong Kong and Singapore through policy documents. After the handover of Hong Kong to China in 1997, the role of Putonghua is far more important than before due to political and economic reasons. However, the medium of instruction for the Chinese Language subject in Hong Kong has long been Cantonese since the British colony days. A change in the medium of instruction from mother-tongue Cantonese to Putonghua is a shift from L1 to L2. This paper will discuss the feasibility of this long term policy of Hong Kong Education Bureau with reference to Singapore’s experience. Currently, Singapore faces the problem of declining standard of reading and writing in Huayu (Putonghua in China) and this paper will investigate the reason for that and suggest possible remedies.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.279

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0060.004
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.194
GPT teacher head0.557
Teacher spread0.363 · 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 designQualitative
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

Citations9
Published2008
Admission routes1
Has abstractyes

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