On Challenges, Decision-Making and Belonging: Exploring the Transnational Identity Formation Experiences of Brazilian Student-Migrants turned (Im)migrants to Canada
Bibliographic record
Abstract
Science without Borders (SWB)/Ciência sem Fronteiras (CsF) is a Brazilian migration-for-development program that has sent post-secondary students to Canada or other developed countries around the world to study STEM subjects. The program began in 2011 and has seen thousands of students participate in coming to Canada. Despite the requirement to return to Brazil immediately after the study period in Canada has ended, some former SWB participants have decided to (im)migrate back to Canada, facing new experiences and challenges when compared to their first time in the country. In this paper, I use a transnational lens to explore the factors affecting transnational identity formation for this group of student-migrants turned (im)migrants to Canada. I argue that, by examining the factors that affect the development of transnational identities for (im)migrants, we can better understand the different routes that (im)migrants take and why. My findings indicate that the types of challenges participants faced were both an outcome and a cause of their agency and the development of their transnational identities. The decision-making strategies that participants used to respond to various challenges were also relevant to the process of negotiating these identities and developing a sense of belonging to one or more places. This research draws on findings from the analysis of personal accounts and self-perceptions obtained through semi-structured qualitative interviews with sixteen former SWB participants who have returned to Canada.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.017 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.008 |
| 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".