Transnational lives interrupted: The Canadian state and Indian international student experiences during the COVID‐19 pandemic
Bibliographic record
Abstract
Abstract Canada has emerged as a major education destination for international students from across the world. International students are understood to significantly contribute towards the labour market and economic growth of Canada including the higher education sector that has come to financially rely on international students. India has emerged as the largest source of international students to Canada in recent years making them a significant category of migrants impacted by the covid‐19 pandemic and the subsequent lockdown measures implemented by the Canadian state. This paper looks at the experiences of Indian international students already in Canada and prospective students in India planning to pursue their studies in Canada. The pandemic delayed international education plans for many students in India while causing significant disruption to the studies, employment, and living arrangements of international students in Canada. Such disruptions created considerable uncertainty over their financial situation including meeting various eligibility requirements for work permit after graduation. This paper reveals the deeply embodied and personalised consequences for students in the face of state responses to balancing pandemic control and retaining financial and economic contribution during the pandemic.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.051 | 0.016 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".