Determinants of Bank Closures: Exploring the Relationship between Neighborhood Characteristics and Bank Branch Locations
Bibliographic record
Abstract
Retail Banking in Canada has experienced significant changes perpetuated by both digital trends in retail and changes in consumer demand. These changes have resulted in significant decreases in client interactions with physical branches in favor of digital platforms (online, mobile, phone banking). As the “Big Five” Canadian banks pursue network optimization strategies focused on reinvesting savings into their digital channels, branch closures will accelerate, resulting in market gaps. Thus, the central aim of this study is to understand the relationship between neighborhood characteristics (built environment and socio-economic) and bank branch locations. Using the city of Toronto as a case study, this research addresses three objectives: (i) to identify neighborhoods underserviced by the “Big Five” Canadian banks; (ii) to examine the spatial relationship between neighborhood characteristics and branch locations; and (iii) to quantify the key neighborhood characteristics linked to branch locations. This study finds that financial exclusion continues to be associated with local dynamics of physical topography, road network, demographics, and socio-economic status. While financial exclusion is becoming a growing area of concern for policy makers, this research finds that access to affordable financial services still proves to be an issue that requires attention.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".