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Record W2793048954 · doi:10.5539/elt.v11n2p193

Role of Email in Intercultural Communication of Criticism in a Chinese English Curriculum Reform Context

2018· article· en· W2793048954 on OpenAlexvenueno aff
Linqiong Lv

Bibliographic record

VenueEnglish Language Teaching · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPolitenessCriticismPsychologyCurriculumContext (archaeology)Intercultural communicationSilencePedagogyConstructive criticismSocial psychologyLinguistics

Abstract

fetched live from OpenAlex

Western teachers working in China often experience cultural conflicts arising from, for instance, the ways that Chinese students perceive face and express criticism. To better understand these face-concerned conflicts, this paper explores the role and significance of email for a group of Chinese students to communicate pedagogical criticism with their western teacher as part of an undergraduate program in Communicative Language Teaching (CLT). A quantitative and qualitative examination of the politeness strategies employed by the Chinese students in their critical emails revealed the three roles of email: email as a safe, polite and effective channel for the Chinese students to express critical views directly (without turning to a third party) and collectively (on behalf of the other students), email as a major means for their western teacher to be informed about problems privately, and email as a springboard for the western teacher to communicate later with more other students publicly. What was criticized in the emails indicated the fundamental disparities in their perceptions of knowledge, the identity of English, and the classroom behavior of silence. Interpretation and discussion of findings were informed by the studies of Chinese psychology and the writer’s insider knowledge gained from her four-year longitudinal participant observation.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.215
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.268
Teacher spread0.261 · 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 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

Citations9
Published2018
Admission routes1
Has abstractyes

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