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Record W2902098401 · doi:10.3968/10570

Academic Writing Difficulty of Chinese Students: The Cultural Issue Behind Chinese and British Academic Writing Styles

2018· article· en· W2902098401 on OpenAlexvenueno aff
Zhang Zhi-long

Bibliographic record

VenueStudies in literature and language · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAcademic writingArgument (complex analysis)Style (visual arts)ComprehensionPsychologyAtmosphere (unit)Writing styleMathematics educationOrder (exchange)Cultural backgroundPedagogySociologyLiteratureLinguisticsResearch methodologyArtMedicine

Abstract

fetched live from OpenAlex

In the contemporary society, an increasing number of Chinese students pursue higher education in overseas countries. Through the observation of certain Chinese students in the UK, the author found that English academic writing style brought huge obstacles to these learners. Under such background, this study was conducted in order to help these students better adapt to English academic atmosphere. The author reviewed previous studies and noticed that learners’ cultural background influenced their writing mode. By the comparison of national cultures and writing styles, it is found that the main differences of Chinese and British academic writing styles exist in students’ comprehension of academic argument, the way of utilizing others’ achievements, and the structure of academic writing styles.

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)0.008
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 score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.351
Teacher spread0.337 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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