People – Money Co-movement and the Ethnic Financial Sectors in Canada and the U.S.
Bibliographic record
Abstract
Financial globalization and international migration have altered the socio-economic-demographic make-up as well as the financial dynamics in immigrant receiving countries. An outcome is the emergence or strengthening of a formal ethnic financial sector consisting of financial institutions that are owned and/or operated by a variety of ethnic groups. Focusing on ethnic banks in Los Angeles, USA and ethnic credit unions in Toronto, Canada, and using secondary sources and interviews with bank executives, this paper demonstrates that contemporary financial dynamics pertaining to immigration is rooted/localized in different ways with different groups, and is shaped by different regulatory and institutional contexts. Specifically, it identifies that ethnic financial institutions in both cities serve co-ethnics first and foremost, and utilize ethnic assets, bonding social capital in particular, to develop their customer base while branching out to other groups by developing bridging social capital and broadening their product lines. The comparison also shows that ethnic banks in LA are larger, more numerous, and mostly Asian-owned, whereas the ethnic credit unions in Toronto are smaller, less numerous, and mostly of European background. These variations stem from differences in the national financial regulatory regimes and in the immigrant population 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 | 0.002 | 0.006 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".