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Record W2605481762 · doi:10.5539/ass.v13n5p158

Role of EMI and Resultant Impact on Career Development of Chinese Students

2017· article· en· W2605481762 on OpenAlexvenueno aff
HE Xiao-yong, Lixia Wen, Ravikiran Honavar Divyashreem

Bibliographic record

VenueAsian Social Science · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsEMIInternationalizationChinaInternationalization of Higher EducationMainland ChinaBusinessMarketingPolitical sciencePublic relationsPsychologyComputer scienceTelecommunicationsElectromagnetic interferenceInternational trade

Abstract

fetched live from OpenAlex

With the ever-changing trends in internationalization of higher education in the recent past, English has been adopted as an effective medium of instruction for many students in various universities all over the world. However, with all its merits, evidence shows that English medium of instruction (EMI) is not very much popular mode of learning in universities across mainland China.This research is an endeavor to highlight the merits of EMI and its associate linkages to career development of Chinese students. While comparing with Chinese medium of instructions (CMI), the findings of this paper have suggested that EMI has a positive impact on the career development of students. Those students who have taken courses in English medium have received better jobs and more opportunities for fair career growth. It is further argued that EMI not only assist the individuals in shaping their better future, but it also facilitates in providing efficient labor force for the growing economies. As part of globalization, large numbers of firms are pouring into China. Resultantly, various companies are vigorously searching for graduates with high level of English proficiency. To this end, the role of EMI programs is highly significant in providing right candidates for the corporate sector. Given the need of current employers, it is highly recommended to establish more EMI programs in Chinese institutions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.925
Threshold uncertainty score0.859

Codex and Gemma teacher scores by category

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

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 head0.021
GPT teacher head0.317
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 teacher head, 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

Citations2
Published2017
Admission routes1
Has abstractyes

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