Exploring research cultures through internationalization at home for doctoral students in Hong Kong and Sweden
Bibliographic record
Abstract
Cultural skills are fundamental to developing global academic scholars. Internationalization at home can facilitate the acquisition of these skills without students having to go abroad. However, research on the effect of internationalization of higher education is scarce, despite apparent benefits to incorporating cultural sensitivity in research. Further, little is known about the role information and communication technology plays. In this pilot study, we describe the experience of doctoral students with an internationalization-at-home program, and its impact on developing an understanding about different research cultures. Eight doctoral nursing students from Sweden and Hong Kong participated in five webinars as "critical friends". The study followed a descriptive, qualitative design. The results demonstrated that students observed cultural differences in others' research training programs. However, while cultural differences reinforced friendship among local peers, they challenged engagement with critical friends. Challenges led to the perception of one another not as critical friends but as "distant" friends. We discuss the possible reasons for these outcomes, and emphasize a need to nurture connectivity and common goals. This would prepare students to identify, translate, and recognize cultural differences to help develop knowledge of diverse research cultures.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".