“We Did It Right on Time”: International Students’ Internship in China During COVID-19 Pandemic
Bibliographic record
Abstract
Enormous distractions brought by deadly COVID-19 pandemic in higher education left no excuse for internship activities. Hence, tradition/offline internship has been postponed or rescinded and a massive online/virtual shift of internships has been observed in lieu. The present case study employed qualitative research approach to solicit information from two (n = 2) internship organizers for international students of a selected university in China. The university continued to implement offline internship as intended right on time in spite of strict curb measures to contain COVID-19. The study revealed; pre-internship briefing, effective communication with receiver institutions, as well as obedience to new normal pandemic prevention measures were the main reasons facilitated on time and offline internship. Simultaneously, difference in educational experiences, language barriers and some movement restrictions within the school were uncovered challenges for interns as international students. Provision of pre-internship briefing, psychological support and counsel, and follow up of rules and procedures were emphasized as recommendations for improving internship experience and in case of upcoming pandemic crises.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".