Transnational Remittance Practices Among Latinos: Analyzing Differences Based on Nativity, Generational Status, and Social Capital
Bibliographic record
Abstract
Using data from the Chicago Area Survey (CAS), our study explores the impact of individual characteristics and factors linked to assimilation and ethnic attachment on the financial behaviors of Latinos in the United States. We particularly examine generational variations in the amount and determinants of remittance behavior among Latinos in the Chicago Metropolitan area. Our findings reveal distinct generational patterns in remittance practices as a transnational activity. While second-generation Latinos engage in remittance activities comparably to their foreign-born parents, a significant decrease is observed in third and subsequent generations. Interestingly, remittance behavior appears to be inversely related to traditional assimilation measures. Contradicting straightforward assimilation theories and aligning more with a transnational viewpoint, our multivariate models suggest a positive correlation between remittance activities and various integration indicators for both foreign-born and U.S.-born Latinos in the Chicago area. While a generational decline in remittance behavior supports assimilation theory, the positive ties between socioeconomic status or assimilation indicators and remittance activities prompt a reevaluation of the assimilation model's dominance. Our results suggest a complex interplay between assimilation processes and remittance behavior, indicating that the latter doesn't necessarily decrease as the former progresses. This calls for more research into the intricate relationship between these two dynamics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".