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Record W2063149329 · doi:10.5539/ass.v5n6p103

Research Raising the Bilingual Teaching Level of Chinese in College

2009· article· en· W2063149329 on OpenAlexvenueno aff
Jing Sun

Bibliographic record

VenueAsian Social Science · 2009
Typearticle
Languageen
FieldComputer Science
TopicHigher Education and Teaching Methods
Canadian institutionsnot available
Fundersnot available
KeywordsChinaBridging (networking)Raising (metalworking)DisciplineForeign languageBilingual educationTraining (meteorology)Chinese languageSet (abstract data type)BusinessPolitical sciencePublic relationsEconomic growthPsychologyMathematics educationEconomicsComputer scienceEngineeringLinguisticsLaw

Abstract

fetched live from OpenAlex

China’s admission to the World Trade Organization (WTO), compounded by the rapid global economic growth – over the last decade – had created exuberant need for skilled manpower in China. This is evident by the demand for skilled talent in inter-disciplinary fields related to foreign language training. Mastering foreign language is considered a scarce resource in China as these specialist skilled persons will provide an important trans-culture bridging link with the Western World. The need for such niche skilled manpower had created challenges to Chinese universities to develop accelerated bilingual training programs to cater for such a demand. China’s Education Authorities recognize this requirement and had set priorities to administer the local universities to provide such bilingual courses. This article has discussed the professional course bilingual education necessity, analyzes concrete questions which Chinese universities bilingual education exists, and proposed the measures and the suggestions which raises the standard of bilingual education level.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.171
GPT teacher head0.494
Teacher spread0.324 · 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

Citations3
Published2009
Admission routes1
Has abstractyes

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