The Geography of Ethnic Residential Segregation: A Comparative Study of Five Countries
Bibliographic record
Abstract
Few studies have undertaken rigorous comparative analyses of levels of ethnic residential segregation across two or more countries. Using data for the latest available censuses (2000–2001) and a bespoke methodology for such comparative work, this article analyzes levels of segregation across the urban systems of five major immigrant-receiving, English-speaking countries: Australia, Canada, New Zealand, the United Kingdom, and the United States of America. After describing the levels of segregation in each, the article tests a model based on generic factors that should influence segregation levels in all five countries and then evaluates—for the urban population as a whole, for the “charter group” in each society, and for various ethnic minority groups—whether there are also significant country-specific variations in segregation levels. The findings show common factors influencing segregation levels in all five countries: notably the size of the group being considered as a percentage of the urban total, but also urban size and urban ethnic diversity, plus country-specific variations that cannot be attributed to these generic factors. In general there is less segregation in Australia and New Zealand than in the other three countries.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".