Transit-Induced Gentrification in Weston and Mount Dennis: A Mixed-Methods Analysis
Bibliographic record
Abstract
As Toronto commits to increase investments in rapid transit across the Greater Toronto Hamilton Area (GTHA), there is an increasing need to ensure existing residents are able to benefit from these new connections. Weston and Mount Dennis are two examples of neighbourhoods that have received major public transit investment and are susceptible to significant neighbourhood change. Most transit-induced gentrification studies depend on quantitative analysis, with little to no consideration for nuanced qualitative examination and often underestimate the number of displaced residents. For this reason, we conducted a mixed-methods study to understand what extent public transit investment has contributed to processes of gentrification in Weston and Mount Dennis. Analysis of census data determined were that there were no conclusive signs that gentrification has occurred in these neighbourhoods as of 2016. Interviews with key neighbourhood stakeholders revealed detailed accounts of neighbourhood change occurring in these areas before, during and after construction of new transit. While the quantitative and qualitative analysis rendered different findings, this outcome provides us with additional data to assess the strengths and weaknesses between different research methods to better understand the most efficient ways to measure gentrification moving forward. Our findings indicate that census analysis does not conclusively indicate gentrification has occurred while interviews with key stakeholders provide perspectives that indicate early signifiers of gentrification in these neighbourhoods.
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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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".