Cheque Cashing Places: Preying on Areas with High Crime
Bibliographic record
Abstract
With the closure of mainstream bank branches in low-income neighbourhoods, cheque cashing places (CCPs) grew exponentially in the past decade. CCP users tend to be those in need of quick cash or who frequently live from pay cheque to pay cheque. CCPs appear to target low-income vulnerable consumers—the so-called “unbanked”. Such individuals are more likely to reside in high-crime areas. We hypothesized that CCPs are more prevalent in neighbourhoods with high crime rates, and that this might be a function of strategic marketing by CCPs, rather than merely an indicator of economic disparity. We explored the relation between the density of CCPs in each census tract in Toronto and its association with both any crime and also violent crime. The findings indicate that CCPs are more abundant in areas of high crime, and especially, violent crime, and this appears to be independent of measures of material deprivation and residential instability. While the CCP industry has strategically focused on customers of low socioeconomic status, it is plausible that they also focus on high-crime areas as well.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".