Contextualising international student migration to Canada: the case of Indian Punjab youths
Bibliographic record
Abstract
The magnitude and persistence of the international student flow from the Indian Punjab to Canada over the last decade presents a compelling case for International Student Migration research, yet empirical data on this specific phenomenon is scant. We address this gap through a thematic analysis based on interviews with 34 Punjabi students aspiring for Canadian education. We investigate Punjabi student migration as the complex interplay between individual motivation and the social and territorial structures in which students are located when they decide to migrate. We apply the aspirations-capabilities framework to highlight the ways in which bounded individual agency is exercised by students within national and international policy contexts. Our analysis reveals a blurring of the line between ‘migration for education’ and ‘education for (im)migration’. While many students sought economic benefits, they also aimed to escape Punjab for personal growth in a more ‘liberal’ and ‘modern’ society, a desire especially strong among women. At the micro level, students’ perceived capabilities (e.g. part-time work, reliance on social networks), influence their real financial capabilities; at the macro level, Canada's previously permissive immigration policies made it a preferred destination. However, recent restrictive changes in immigration policy create new challenges for Punjabi students’ education-migration aspirations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.042 | 0.012 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".