Bibliographic record
Abstract
The coronavirus (COVID-19) pandemic has forced instructors and students to work together under constantly evolving circumstances. The abrupt transition to online education has contributed to making the educational experiences of instructors and students more emotionally complex and intense. Growing attention has been directed toward understanding the challenges international students face and their impact on the students’ learning experiences, considering the unprecedented difficulties the global pandemic has posed for international student mobility. In this context, instructors are in a unique position to support international students. One way to do so is by being (more) empathetic. Empathy is important because it not only helps us feel for and with the other, but also improves the academic outcomes of students. This paper discusses the importance of empathy in teaching international students by expanding on the concept of teacher empathy. This paper also critically examines the experiences of international students in higher education in several domains of lived experience, such as the linguistic, academic, social, cultural, and psychological. Other aspects of empathy presented are its contagious nature and the concept of radical empathy. This paper concludes by highlighting the practical application of empathy in light of international students’ experiences.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".