Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".