Aboriginal Settlement Patterns in Canadian Cities: Does the Classic Index-Based Approach Apply?
Bibliographic record
Abstract
Based on the evidence obtained in qualitative studies, Massey and Denton argued in their 1988 paper (“The dimensions of residential segregation” Social Forces67 281–315) that the residential segregation of ethnic or racialized groups is a complex and multidimensional phenomenon and, therefore, should be measured along several dimensions simultaneously. In a systematic review and analysis of all segregation measures proposed to that date, they identified five conceptually distinct dimensions of ethnic segregation—evenness, exposure, concentration, centralization, and clustering—and ‘best indices’ to measure them. This index-based approach has achieved a ‘canonical’ status and has been employed in numerous studies of segregation patterns. However, it is often overlooked that the structure of ethnic residential segregation uncovered by Massey and Denton is specific to the context in which segregation takes place—that is, the residential segregation of ethnic and racialized groups in US cities. This paper attempts to assess the utility of Massey and Denton's five-dimensional structure of segregation for the study of settlement patterns of Aboriginal people in Canadian metropolitan areas. We find that the application of the Massey and Denton model to the urban Aboriginal population in Canadian cities produces a significantly different structure of segregation.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.031 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".