Gendering international student mobility: an Indian case study
Bibliographic record
Abstract
This thesis explores the dialectical relationship between gender and \ninternational student mobility (ISM). The focus is on the experiences of Indian \nstudents across three space-time locations: before the students left India; while \nabroad in Toronto; and their return to New Delhi. The value of this research is \ntwo-fold. Firstly, my research helps to fill the lacuna in ISM research that \nexamines the phenomenon through a gender optic. Secondly, there is \nincreasing interest in Canada and other countries – evident in the media and \ngovernment policy – in international students from India. \nThe study is located at the nexus of gender and mobility scholarship; it adopts \nGendered Geographies of Power as a foundational framework. The research \nemployed a multi-sited, mixed-methods approach to data collection. The data \ncollection in the field sites of Toronto, Canada and New Delhi, India consisted of \nin-depth semi-structured interviews and participant observations. An online \nsurvey was mounted for the duration of the fieldwork to gather data on the \nbroader population of Indian students abroad. The results of this survey provide \ncontext for the discussion in three empirical chapters. \nThe first of the three empirical chapters explores the impact of gender relations \nin shaping motivations to study abroad. The second chapter examines how \nrelations of power in and across multiple spaces (re)shape the students‟ \nperformances of gender identities in everyday life in Toronto. The final \nempirical chapter examines the students‟ experience of return mobility as they \nattempt to adapt to a different (but familiar) gender context again. \nMy research contributes to the growing body of scholarship on ISM as well as \nthat on gender and migration. By employing a gendered perspective, the indepth \ninterviews as well as ethnographic research reveals the shifting \nsubjectivities of the migrants as they simultaneously negotiate multiple ethnic \nand kinship interactions in their everyday lived experiences. Secondly, the \nonline survey presents the gendered class configurations of the socio-economic \nbackground of the Indian international students. Lastly, the „return‟ experiences \nof the students are differentiated by gender: more women than men found it \nharder to (re)negotiate their gender-expected performances in New Delhi. \nFurthermore, the „return mobility‟ of men appears to be more permanent than \nthe return mobility of women.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".