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Record W3180829530 · doi:10.23977/aetp.2021.54012

Teachers’ Role Positioning and Coping Strategies in the Context of Intercultural Teaching

2021· article· en· W3180829530 on OpenAlexvenueno aff
Lulu Kong

Bibliographic record

VenueAdvances in Educational Technology and Psychology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsIntercultural competencePedagogyIntercultural relationsPsychologyIntercultural communicationCoping (psychology)Teaching methodIntercultural learningCompetence (human resources)Social psychology

Abstract

fetched live from OpenAlex

With the increasingly close global relationship, intercultural teaching has become an inevitable trend in current education development. The cultivation of students’ intercultural competence is bound to become one of the important goals of foreign language teaching. Intercultural teaching will present new characteristics because of the deepening of more intercultural factors, and it will also affect the interrelationship between teaching elements. As the main body of practice in the teaching process, the role of teachers will be different from traditional teaching. Facing the background of the era of intercultural teaching, teachers should adapt to the needs of the era and clarify their role positioning. This article first reviews the rich connotations of intercultural competence, analyzes the new characteristics of intercultural teaching. Then starting with the changes in the relationship between teachers and teaching elements, the article analyzes the role of teachers in intercultural teaching, and proposes coping strategies and suggestions for teachers to better adapt to the needs of intercultural teaching.

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)0.004
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.003
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
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 head0.020
GPT teacher head0.406
Teacher spread0.386 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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