On a Contrastive Method of Teaching Culture in ELT Classroom for College-Level Students
Bibliographic record
Abstract
It has become an agreement that language teaching is reinforced when ESL teachers have an awareness of incorporating culture teaching in the classroom. Language and culture are intertwined. In any language, it is more than just words that convey meaning. All cultures have their preferences, practices, values and traditions that interwoven with the language. From the humanistic perspective, the education of different cultures aids students in getting to know different people, which is necessary for understanding and respecting other peoples and their ways of life; therefore contrastive study between the two languages and cultures is imperative for ESL teachers. Students would master the second language better, if teachers have an adequate understanding of both native and target culture and actively spread it. The aim of language teaching is more than the manipulation of syntax and lexicon but to foster well-rounded students that can understand and respect other cultures at the same time spread Chinese culture to promote the communication and interaction between China and western world. In terms of methods of teaching culture in college-level ESL classroom, it would be more effective that teachers design a series of students “hands on” activities. Teachers can make those cultural features an explicit topic of discussion rather than being taught implicitly, imbedded in the linguistic forms. Key words: Language and culture; Contrastive study; Culture instruction activities
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".