Narratives of Learning and Resisting: An Intersectional Analysis of Lived Experiences of First-Generation South Asian Female International Graduate Students
Bibliographic record
Abstract
This study was influenced by my own experience as a first-generation South Asian female international graduate student in Canada. As I reflected on my experiences as part of an assignment for one of my Master of Education courses, I became interested in experiences of others who have followed a similar path. Namely, first-generation female international graduate students from South Asia. A literature searches of education databases at the University of Windsor Leddy Library led me to the conclusion that while several studies have examined experiences of international students in Canadian post secondary institutions (Chataway & Berry, 1989; De Moissac et al., 2020; Oloo, 2022; Pidgeon & Andres, 2005), there is a gap in research that relate specifically to experiences of first-generation South Asian female international graduate students in Canada. Grounded in the community cultural wealth (CCW) theoretical model (Yosso, 2005), this narrative inquiry employs semi-structured interviews as conversations to explore experiences of six study participants. Intersectional and thematic analysis of participant narratives resulted in 13 “inductively” identified themes which emerged from the data itself in the domains of Opportunities, Challenges, and Overcoming Challenges. And six “deductively” identified themes which mapped onto the six domains of the CCW model that were identified in the participant narratives.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| 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".