Enhancing the Cross-Cultural Competence of Prospective Language Teachers
Bibliographic record
Abstract
In the twenty-first century, education does not merely focus on information exchange; additionally, it does so on various abilities and living in harmony. To materialize such acquisition among students, cross-cultural competence is an essential vehicle in a rapidly globalizing world. This calls for integrating comprehensive cross-cultural education as an independent subject into teacher training programs alongside the applied practices that go with it. Against this backdrop, the current study aims to determine the cross-cultural competency capabilities of Turkish language teacher candidates studying at one of the major universities in Turkey. In this article, initially cultural dimensions, scope of culture, and aspects of cross-cultural competency are addressed on a theoretical basis. In addition, learning materials are developed by the candidates, based on the instructions provided by the researcher, and analyzed according to their content of cross-cultural competency. The paper also discusses the cultural background of the candidates and their cross-cultural competency capabilities. The findings show that the participants have major difficulty presenting sufficient information or content in developing their cross-cultural competency. In the end, there are recommendations for enhancing the cross-cultural competency capabilities, while shedding light on the inadequate focus devoted to improving these skills within the training programs.
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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 | 0.003 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".