Between worlds : online transnationalism of highly skilled Mexicans in Vancouver
Bibliographic record
Abstract
This thesis examines the ways in which highly skilled Mexicans in Vancouver participate in digitally enabled transnational activities. I explore how this group uses digital technologies to facilitate their transnational relations, particularly by focusing on how technology contributes to immigrants maintaining a sense of belonging in their country of origin, as they adapt to Vancouver at different stages of their migration journey. Additionally, I explore how highly skilled Mexicans deploy their social, cultural, economic and political capital once they are established in Vancouver to maintain or neglect their ties with Mexico. I use a mixed methods approach including autoethnography, analysis of public statistics, an analysis of Youtube data and 18 semi-structured interviews with highly skilled Mexicans in Vancouver. Throughout this research, I explore the socioeconomic characteristics of Mexican migrants to Vancouver and their interactions with Mexican institutions in Vancouver. I also look at how highly skilled Mexicans use digital technologies to maintain or neglect their transnational relations with Mexico, and the way that the use of these technologies impact their everyday life in Vancouver. To zoom into the role that identity negotiation plays in digital content created by expatriates, I analyze two YouTube channels hosted by Mexicans living in Vancouver. I conclude that a lack of adequate engagement from both the origin and destination nation-states results in a status of limbo for highly skilled Mexican immigrants that creates a need to reduce their vulnerability by choosing to associate with others who share a similar class habitus rather than nationality.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".