Exploration on Cultivation of College Students’ Cross-Cultural Communication Competence in College English Teaching
Bibliographic record
Abstract
In traditional college English teaching, attention is focused on improvement of students’ general English proficiency. Students usually receive knowledge and information passively form teachers without their own analysis and reflection. Worse still, students’ cross-cultural communication competence has been neglected. Cultural information has been regarded as factual knowledge imparted to students which are obstructive to the formation of students’ cross-cultural communication competence. In order to improve students’ cross-cultural communication competence, it is proposed in this paper that teacher should pay attention to improve students’ oral English proficiency so as to engage students in oral communication. Then college English teachers’ teaching methodology should be changed, designing more tasks close to the real situation especially related to the cultural differences for students to explore and organize oral activities. In the process of English teaching, the formation of culture stereotypes and bias should be avoided, and students should be encouraged to read extensively to receive more true cultural information. Proper culture empathy is also feasible.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".