MétaCan
Menu
Back to cohort
Record W1714103588

A Cultural Perspective Study on CIE Specialty English Teaching Mode

2015· article· en· W1714103588 on OpenAlexvenueno aff
Xiaoqiu Han, Hongli Xian

Bibliographic record

VenueCross-cultural communication · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsSpecialtyChinaCultural competenceSociologyCommunicative competencePedagogyPerspective (graphical)Foreign languageChinese cultureLanguage educationValue (mathematics)Mathematics educationPsychologyPolitical scienceLawComputer science
DOInot available

Abstract

fetched live from OpenAlex

Chinese International Education specialty in Chinese higher education is nowadays confronted with some tough missions, especially teaching mode establishment of the specialty English as a second language. Based on cross-cultural communicative theories and built-in functional requirements of English teaching, we will in this article explicate the cardinal causes and ways of improving of the competence of the students of this specialty to express Chinese culture with English. Over two years’ experimental teaching practice in International Cooperation and Exchange School, Qiqihar University of China, we are convinced, endorsed by our satisfactory feedback and teaching effects, that introducing traditional culture of China into English class is advisable, and the practice is also compulsory for the cultivation of bilingual interdisciplinary talents who will be committed to the development of the least prejudiced cultural exchanges and the transmission of Chinese culture with philanthropism and universal value. Therefore it is of great practical worth for foreign language teaching and global social significance of cultural spreading.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.004
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.074
GPT teacher head0.374
Teacher spread0.300 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueCross-cultural communicationSame topicSecond Language Learning and TeachingFrench-language works237,207