The Suburban Origins of Redlining: A Canadian Case Study, 1935-54
Bibliographic record
Abstract
Redlining occurs when institutions decline to make mortgage loans in specific areas. The practice originated in the 1930s, when federal agencies encouraged lenders to rate neighbourhoods for mortgage risk. Since the 1960s, especially in the US, it has been associated with disinvestment, racial discrimination and neighbourhood decline. It has always been viewed as a feature of the inner city. Historical evidence indicates that across Canada the first areas to be redlined were the less-desirable suburbs. Land registry and property assessment data establish the emergent patterns in Hamilton, Ontario. Between 1931 and 1951, institutional lending became a social norm first on new dwellings in suburbs. Individual lenders, previously dominant, were relegated to older inner-city properties or cheaper dwellings in less-desirable suburbs. In 1931, there were only minor geographical variations in the incidence of mortgage finance, and specifically of institutional financing, across the urban area. By 1951, lending institutions, led by insurance companies, were discriminating sharply in favour of the West End, the Mountain and Bartonville, and against those parts of the East End that were unserviced or close to lakefront industry. The evidence for Hamilton confirms that in Canada redlining originated in the suburbs. The same may also be true for US metropolitan areas, although the institutional context was different and relevant data are lacking.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".