Building Bridges: The Role of Human Capital and Social Capital in the Migration Experience of Mexicans in the Vancouver Metropolitan Area
Bibliographic record
Abstract
Migration is a process that begins with the mere thought of moving, but it continues long after the individual arrives in her or his new home. The process is constrained by certain factors such as capital, immigration policy, and the existence of kinship networks. Individuals who are able to overcome these constraints and decide to migrate, must overcome a new set of challenges upon arrival in the host county. These challenges include the need to adapt to a new labour market, use of a new language, and integration with the rest of society. Human and social capital are important tools that allow immigrants to successfully meet these challenges. Human and social capital play different roles in the migration process of these individuals. Human capital allows Mexican individuals to overcome the barriers to initial migration, but it does not ensure successful social or labour market integration. Social capital is a more effective tool in the resettlement process, and it also helps to strengthen transnational bonds. The Mexican community in the Vancouver CMA does not rely on a complex set of kinship networks. However, this study found that there is an ongoing process to create social capital. This process simultaneously encourages the formation of nationality-based social capital (i.e. bonding social capital) and bridging social capital. These types of capital are important because they help the community to overcome the challenges of integrating into the labour market as well as the larger society. Furthermore, the person-to-person contact between Mexicans and the rest of society fosters mutual understanding. Since much of the Mexican community maintains strong ties to the source country, integration is an important point of reference for further engagement between Mexico and 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".