Bibliographic record
Abstract
As universities continue to internationalize their curricula and recruit a growing number of international students, instructors facilitate learning in increasingly diverse classrooms. This chapter explores the application of Intercultural Teaching Competence (ITC) by faculty members across the disciplines at a large Canadian research university. Based on focus group interviews with instructors in eighteen disciplines, it provides varied and concrete examples of how instructors mobilize intercultural teaching competence to navigate diverse classrooms, promote perspective-taking and global learning goals among students, practice culturally relevant teaching, and validate different ways of knowing and communicating among students through assessment practices. Placing disciplines at the centre of the discussion in this way elucidates the extent to which ITC may be adapted to fit the contours of the academic field and allows readers to explore best practices for facilitating the development of intercultural competence among students in their disciplines. Finally, the implications of disciplinary differences in ITC are discussed for faculty development and curriculum support.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".