From ‘black‐balling’ to ‘marking’: the suburban origin of redlining in Canada, 1930s–1950s
Bibliographic record
Abstract
Redlining occurs where institutional mortgage lending is the norm and where lenders decline to loan in specific areas. The term originated during the 1930s in the United States, where it was promoted by federal agencies and acquired racial connotations. Recently, redlining has been uncommon in Canada, but contemporary reports, archival records and parliamentary debates show it was widespread from the 1930s to the 1950s. Contemporaries distinguished the ‘black‐balling’ of remote areas, where it was impossible to make loans, from the ‘marking’ of districts in urban areas where companies could lend but chose not to do so. Only the latter can be regarded as redlining. As defined by Canadian housing agencies, most ‘marked’ areas were in unregulated, owner‐built suburbs that lacked services. The suburban origin of redlining in Canada had little to do with the distribution of ethnic minorities. As long as it lasted, roughly until the late 1950s, it perpetuated social class diversity in Canadian suburbs.
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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 | 0.003 | 0.006 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".