Fostering Successful Integration and Engagement Between Domestic and International Students on College and University Campuses
Bibliographic record
Abstract
As the number of international students pursuing higher education abroad continues to increase globally (OECD, 2017), college and university campuses have the potential to serve as key spaces of cross-cultural learning and the cultivation of international friendships. Yet spatial proximity and intercultural contact do not always result in meaningful interactions between different social groups (Wessel, 2009). Various studies have shown that interactions between domestic and international students rarely result in cross-cultural friendships within higher educational settings (Trice, 2004; Gareis, 2012; Rose-Redwood & Rose-Redwood, 2013). This disconnect between international students and host communities is often attributed to the failure of the former to “adjust” to the latter. However, as Ryan (2011) argues, international students are not simply “problems” in need of a solution by university administrators but rather “provide an opportunity for the co-construction of new knowledge and more collaborative ways of working and thinking” (p. 631 and 642). While much attention has been devoted to the challenges that international students face, there is also a need for scholars to consider innovative pathways toward building meaningful relationships between domestic and international 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.030 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".