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A Content Analysis of the Cultural Content in the EFL Textbooks

2010· article· en· W1899322227 on OpenAlexvenueno aff
Juan Wu

Bibliographic record

VenueCanadian social science · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesIntercultural communicationCultural competencePedagogyCompetence (human resources)SociologyPsychologyPhilosophySocial psychology

Abstract

fetched live from OpenAlex

Intercultural communicative competence has been widely accepted as the aim of EFL teaching. In order to obtain this competence, Culture teaching must be implemented in the college English teaching. Textbooks have played a very important role in the course of teaching and learning. Textbooks introduce students the cultures of different countries and regions, thus making it convenient for students to exert a relatively remarkable influence on the fostering of students’ cultural awareness and competence of intercultural communication. This paper focuses on the content analysis of the cultural content in College English (New) (henceforth, CE (New)). Key Words: College English (New); content analysis; textbook evaluation Resume: La competence de communication interculturelle a ete largement acceptee comme le but de l'enseignement de l'anglais. Afin d'obtenir cette competence, l'enseignement culturel doit etre applique dans l'enseignement de l'anglais dans les colleges. Les manuels ont joue un role tres important dans le cadre de l'enseignement et de l'apprentissage. Les manuels initient les etudiants aux cultures de differents pays et regions et exercent une influence relativement remarquable sur la sensibilisation culturelle et les competences de communication interculturelle des eleves. Le present article se concentre sur l'analyse du contenu des contenus culturels dans les manuel de l'anglais aux colleges (Nouveau) (desormais, on utilise AC (nouveau) pour designer l'anglais de college).Mots-cles: anglais de college (nouveau); analyse du contenu; evaluation des manuels scolaires

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.110
GPT teacher head0.276
Teacher spread0.166 · 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 designTheoretical or conceptual
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

Citations54
Published2010
Admission routes1
Has abstractyes

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