Is There a Credit Union Difference? Comparing Canadian Credit Union and Bank Branch Locations
Bibliographic record
Abstract
This study of credit union and bank branch locations and neighbourhoods in Canada seeks to discover if there is a distinct credit union niche. The study builds on an earlier paper of credit unions and banks in the US which found that credit unions in Wisconsin, Arizona and New Hampshire were more likely to be located in lower-income areas than bank branches (Mook, Maiorano & Quarter, 2015). In Canada, we find that credit union branches are over-represented in rural areas, and under-represented in large population centres relative to bank branches. Additionally, credit unions are overrepresented in middle income areas and underrepresented in high income areas compared to bank branches both at the national level and in all provinces where differences are statistically significant. Another significant finding is that while both credit unions and banks cater to marginalized communities, the type of marginalized communities they cater to distinguishes them. Making use of the Canadian Marginalization Index, we find credit union branches in Canada to be overrepresented in communities marginalized along the dimensions of Material Deprivation and Dependency, while bank branches are overrepresented in communities marginalized along the dimension of Residential Instability and Ethnic Concentration.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".