Bibliographic record
Abstract
In this paper, we use census tract data to analyse changes in neighbourhood income inequality and residential economic segregation in the eight largest Canadian cities during the 1980-95 period. Is the income gap between richer and poorer neighbourhoods rising? Are high and low-income families increasingly clustered in economically homogeneous neighbourhoods? The main results are an elaboration of the spatial implications of the well documented changes that have occurred in family income and earnings inequality since 1980. We find that between neighbourhood family income (post-transfer/pre-tax) inequality rose in all cities driven by a substantial rise in neighbourhood (employment) earnings inequality. Real average earnings fell, sometimes dramatically, in low-income neighbourhoods in virtually all cities while rising moderately in higher income neighbourhoods. Strikingly, social transfers, which were the main factor stabilizing national level income inequality in the face of rising earnings inequality, had only a modest impact on changes in neighbourhood inequality. Changes in the neighbourhood distribution of earnings signal significant change in the social and economic character of many neighbourhoods. Employment was increasingly concentrated in higher income communities and unemployment in lower income neighbourhoods. Finally, we ask whether neighbourhood inequality rose primarily as a result of rising family income inequality in the city as a whole or because families were increasingly sorting themselves into like neighbourhoods so that neighbourhoods were becoming more economically homogeneous (economic segregation). We find that economic spatial segregation increased in all cities and was the major factor behind rising neighbourhood inequality in four of the eight cities. A general rise in urban family income inequality was the main factor in the remaining four cities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 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".