Bibliographic record
Abstract
Gentrification is a phenomenon that most urban planners are intimately familiar with, and it is a subject that has been documented and debated extensively within academic literature. Proponents argue that gentrification has the potential to increase social mixing and rejuvenate depressed inner city neighbourhoods. Detractors claim that gentrification displaces working-class residents who cannot afford the increases in rent, housing prices, or property taxes. This report examines the relationship between displacement and gentrification through a comparative case study of Montreal and Toronto. Household composition and income characteristics have been analyzed using census data from 1971 to 2001. Consistent with the findings from Higgins (2010), gentrified neighbourhoods in Montreal also exhibited an increase in individual and household income, an increase in the proportion of individuals in higher income groups, and a decrease in the proportion of individuals in lower income groups. In Toronto, individual and household average income rose much higher than it did in Montreal, but changes in housing tenure indicate that gentrification might be more extensive in Montreal than previously thought. This research has implications for planners and policy-makers that should not be underestimated. Widespread displacement of working-class residents is possible if city planners continue to embrace it as a revitalization tool without a strategy to alleviate displacement. Cities therefore have a responsibility to prevent displacement, using policy tools like rent control and inclusionary zoning, and by supporting affordable and non-market housing alternatives.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".