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Record W2171209805 · doi:10.5539/elt.v8n1p21

A Survey on College English Writing in China: A Cultural Perspective

2014· article· en· W2171209805 on OpenAlexvenueno aff
Junhong Ren, Na Wang

Bibliographic record

VenueEnglish Language Teaching · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPerspective (graphical)ChinaSentenceMathematics educationQuestionnaireLinguisticsCollege EnglishPedagogySociologySocial scienceHistory

Abstract

fetched live from OpenAlex

This survey investigates to what degree the Chinese learners know about the discrepancies between Chinese and English thought patterns and their possible effects on English writing. Eighty-one students from North China Electric Power University participate in the survey. Qualitative and quantities approaches, involving the adoption of both questionnaire and data analysis, underpin the survey. Questionnaire is used to examine five constructs, namely, students’ writing condition, students’ knowledge about discrepancies of the Chinese and English thought patterns and their effect on Chinese learners’ English writing in terms of wording, sentence structure and discourse organization. Data collected from the questionnaires are then analyzed. Findings from the study reveal that a vast majority of Chinese students are surprisingly not fully aware of the important influence thought patterns exert on languages and how discrepancies in thought patterns are associated with differences in languages. As a consequence of this, their English essays preserve some features of Chinese despite years of English learning. The results of the study contribute to a good understanding of the Chinese learners’ current writing condition in EFL teaching. Suggestions to alter this undesirable situation are put forward.

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)0.002
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.015
GPT teacher head0.277
Teacher spread0.263 · 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.

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

Citations2
Published2014
Admission routes1
Has abstractyes

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