Credentialized aspirations : the mobility journeys of Mexican professionals in North America
Bibliographic record
Abstract
This dissertation examines the journeys of Mexican professionals to the United States and Canada, with a specific focus on the cities of Seattle and Vancouver. Drawing from 45 interviews with highly skilled Mexicans and their families conducted during 2021-2022, it explores how participants create projects of international mobility throughout their lifetimes, and how these projects influence their aspirations, understandings of citizenship, and transnational identities. It also investigates the strategies employed by these migrants to establish themselves in multiple locations simultaneously and navigating shifts in both upward and downward mobility in a transnational context. With an interdisciplinary approach building on geography, sociology and migration and mobility studies, this research uses a mixed-methods approach to explore the daily lives of professional Mexican migrants. It combines comparative immigration policy analysis, census data analysis, and life history interviews. The research highlights the importance of education as an intangible investment, which, in turn, facilitates highly skilled migration during individuals’ productive years. By analyzing the role of North American migration infrastructures this research shows how skilled migration policies facilitate the transnational transfer of social capital. Lastly, this dissertation yields insights into the challenges faced by professional migrants highlighting in particular the influential role of social class in facilitating what I call “credentialized mobility” in the North American context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".