Ghettos in Canada's cities? Racial segregation, ethnic enclaves and poverty concentration in Canadian urban areas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it