Building, negotiating and sustaining transnational social networks: Narratives of international students’ migration decisions in Canada
Bibliographic record
Abstract
Abstract International student migration (ISM) is one of the fastest growing categories of migrants in Canada. Drawing on the narratives of 30 international students at a Canadian university, this paper investigates international students’ decisions to study overseas and the roles of social networks in shaping mobility. We find that international students negotiate information while embedded in multiple social networks consisting of family, friends, ethnocultural and religious communities, and professional relations in origin and settlement countries. These social networks exceed typical knowledge and connection functions; they act as informal migration agents, providing transnational care and guidance, and ‘do’ family in ways that shape mobility decisions and settlement. The information provided through these networks, however, can be inaccurate or incomplete, requiring the strategic mobilization of new networks to support migration. We conclude that international student mobility relies on building transnational networks to support knowledge transfer, provide care and offer tangible supports. Furthermore, we argue that these fluctuating local and transnational social networks should be more fully recognized in the theorizing of ISM and in strategies implemented for supporting international students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.036 | 0.021 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.010 |
| 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".