The role of human dignity in students’ migration trajectory: a case study of Iranian international students in Canada (Montreal)
Bibliographic record
Abstract
There is a dearth of literature examining the perspective of international students across their migration trajectory and scholars have given little attention to gender analysis in studies on international student migration.This thesis proposes a novel conceptual framework for examining this subject: human dignity.The following three-manuscript dissertation explores how Iranian international students living in Montreal have experienced human dignity in preand-post phases of their migration.In a case study using semi-structured qualitative interviews, 24 Iranian international graduate students (12 men and 12 women) explored the nexus of human dignity in relation to their motivations and experiences within Canadian universities and the immigration system.Data indicate that both women and men students, with minor differences, connect their reasons for migration to maintaining their human dignity.They attributed their motivation to study abroad to their pursuit of freedom of expression, socio economic rights, respect, equality, honor, and fulfillment.A gender analysis of students' experiences of human dignity within Canadian universities reveals some differences regarding perceptions of human dignity.However, contradictions exist between Iranian international students' experiences of human dignity within Canadian universities and in the immigration system.The study results reflect that while students were for the most part satisfied with their experiences at Canadian universities compared to those in Iran, participants reported that the Canadian and Quebec immigration systems did not respect their human dignity and failed to meet their expectations.Given the significant role of human dignity in Iranian international students' migration to Canada, this dissertation includes recommendations for policy makers, social workers, and future researchers.Recommendations include establishing settlement services for international students, extending the duration of international student visa, exercising transparent and supportive communication with immigration applicants, and establishing an independent advisory committee to evaluate the immigration system.The findings of this research reaffirm the responsibility of government and university policy makers in Iran and Canada, as well as social work advocates and researchers to explore human dignity when engaging with communities that have experienced varying precarity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".