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Record W4408988766 · doi:10.5539/hes.v15n2p235

A Prisma-Guided Systematic Review of ICC Development for English Majors in China: Trends, Gaps, and Recommendations for a Specialized Training Program

2025· article· en· W4408988766 on OpenAlexvenueno aff
Jin Qi

Bibliographic record

VenueHigher Education Studies · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsChinaFaculty developmentTraining (meteorology)Medical educationPsychologyMathematics educationProfessional developmentPedagogyPolitical scienceMedicineGeography

Abstract

fetched live from OpenAlex

This PRISMA-guided systematic review explores the development of intercultural communication competence (ICC) in English majors in Jingdezhen, China, for sectors such as tourism, cultural marketing, and ceramic translation. The study adopts the methodology of a systematic literature review, analyzing 22 papers between 2014 and 2024. The review finds students' fundamental ICC, including cultural awareness, communication skills, language skills, and practical experience. It also finds lags in digital literacy, intercultural responsibility, and global citizenship, which are increasingly at the heart of effective international engagement. Research states it would better prepare students for worldwide participation by making ICC training work through experiential learning, information technologies, and intercultural obligations. Research provides stimulating details about flaws within current ICC courses. It emphasizes the need to create a professional training system to render Jingdezhen's English majors more interculturally flexible and internationally competitive, in line with the city's growing global cultural and economic impact.

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)0.148
metaresearch head score (Gemma)0.264
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.148
Threshold uncertainty score0.783

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1480.264
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0120.014
Bibliometrics0.0310.026
Science and technology studies0.0040.004
Scholarly communication0.0070.008
Open science0.0070.007
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0090.001

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 head0.093
GPT teacher head0.389
Teacher spread0.296 · 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 designSystematic review
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
Published2025
Admission routes1
Has abstractyes

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