Intercultural Communicative Competence Awareness of Turkish Students and Instructors at University Level
Bibliographic record
Abstract
Competence in intercultural communication requires an understanding of both the L1 and L2 cultures, and many now believe that language learning is, in many respects, cultural learning. The relationship between language and culture, as well as the role of cultural competence in communicative competence has come increasingly under the microscope and as a result, educators have gained a greater appreciation for the role culture plays in language acquisition. Intercultural communicative competence refers to the ability of an individual to navigate within a given society appropriately. In other words, individuals are able to use appropriate gestures, understand the unspoken meaning within the words, appreciate the cultural underpinnings in any communication, and make appropriate cultural references to aid understanding. From this viewpoint, in order to understand the perception of Intercultural communicative awareness (ICC), a survey was performed among instructors and students of English. This survey was distributed to 42 Turkish instructors of English and 183 Turkish students from the English Language and Literature Department of Karabuk University, a total number of 225 respondents. The participants were aged between 18 and 55. By means of set scientific instruments such as (Anova, Spss, etc.) the data collected from the participants was analysed and evaluated.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".