The ABCs of Teaching Cross-culturally: University Educators’ Experiences
Bibliographic record
Abstract
Not all students have the opportunity to study abroad nor to benefit from having international students in their classes, but they can benefit from having an educator who has taught cross-culturally in an international setting. As Schlein and Garii (2011) explain, educators can use international experiences to become “culturally enhanced” and bring these enhancements back to their classrooms—including (potential) shifts in personal and professional identities. This paper describes the benefits, challenges and advice that 11 university educators offer based on their personal experiences. Given the reported lack of orientation activities, these ABCs may be important in helping to prepare educators considering international projects (as the old idiom goes “forewarned is forearmed”). Further, it can help universities design support services for educators going abroad and for visiting educators to foster a positive experience for the educators and students.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.014 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".