Bibliographic record
Abstract
Intercultural Communication Competence (ICC) is the absolute necessity for talents in the 21st century. Meanwhile, the development of ICC competence has already become a new teaching concept, which will penetrate in all aspects of language teaching activities. Indeed, to facilitate language learners to develop ICC, language teachers, especially those teaching English as Second Language (ESL), should first be competent intercultural communicators with great confidence and sufficient experience.The study is undertaken to discover real picture of the ESL teachers’ ICC competence by measuring their individual system, episodic system, and relational system. Most ESL teachers under the test are strong in their individual system in that they are highly proficient in English, and they have a good deposit of cultural and linguistic knowledge in target language. In this case, they are mostly likely to achieve communication effectiveness in intercultural communication. Nevertheless, they don’t have well-built episodic system and relational system. This is largely due to the facts that they haven’t experienced varying intercultural contexts, and the interpersonal relations with foreigners are inclined to be simple. All these factors contribute to ESL teachers’ incompetence in accomplishing different tasks in intercultural communication, as well as in intercultural education. Therefore, ESL teachers should improve their ICC through real intercultural practices. Besides, the study also tests statistical significance between ICC and overseas experience, and the significance between ICC and frequency of contact with foreigners.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".