Ghettos in Canada's cities? Racial segregation, ethnic enclaves and poverty concentration in Canadian urban areas
Bibliographic record
Abstract
Recent literature suggests a growing relationship between the clustering of certain visible minority groups in urban neighbourhoods and the spatial concentration of poverty in Canadian cities, raising the spectre of ghettoization. This paper examines whether urban ghettos along the U.S. model are forming in Canadian cities, using census data for 1991 and 2001 and borrowing a neighbourhood classification system specifically designed for comparing neighbourhoods in other countries to the U.S. situation. Ecological analysis is then performed in order to compare the importance of minority concentration, neighbourhood classification and housing stock attributes in improving our understanding of the spatial patterning of low‐income populations in Canadian cities in 2001. The findings suggest that ghettoization along U.S. lines is not a factor in Canadian cities and that a high degree of racial concentration is not necessarily associated with greater neighbourhood poverty. On the other hand, the concentration of apartment housing, of visible minorities in general, and of a high level of racial diversity in particular, do help in accounting for the neighbourhood patterning of low income. We suggest that these findings result as much from growing income inequality within as between each visible minority group. This increases the odds of poor visible minorities of each group ending up in the lowest‐cost, least‐desirable neighbourhoods from which they cannot afford to escape (including social housing in the inner suburbs). By contrast, wealthier members of minority groups are more mobile and able to self‐select into higher‐status ‘ethnic communities’. This research thus reinforces pleas for a more nuanced interpretation of segregation, ghettoization and neighbourhood dynamics.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".