Placing the planner in the gentrification discussion : planning interventions in Toronto's downtown west
Bibliographic record
Abstract
While the process of gentrification has been one of the most hotly debated in academia this discussion has been dominated by select voices and disciplines. Little been written from the point of view of the urban planner or explicitly regarding built form and land use. This study firmly situates the planner in the gentrification discussion by analyzing three planning interventions in Toronto's Downtown West. The study's purpose is to provide a clearer understanding of the planner's role and abilities when planning for neighbourhoods facing upscale change. In addition to the case studies, the study provides a general overview of the historical gentrification literature, highlighting what aspects the planner ought to be most concerned about. The study concludes by providing a summary of the inventory of the tools used by the planner in these cases, as well as challenges, problems. and opportunities raised by these cases.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.034 | 0.021 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".