Transit-Oriented Development Surrounding GO Transit’s Stouffville Line Stations in Scarborough: Issues and Prospects
Bibliographic record
Abstract
Transit-oriented development (TOD) policies greatly impact where housing development is focused in the Greater Toronto Area (GTA). Improvements to enhance southern Ontario’s GO Train system are being made throughout the decade and the 2030s. With this enhancement and the related TOD policies for Major Transit Station Areas (MTSAs) that encourage density, more housing development is beginning to form. These governmental policies that encourage transit ridership through increased accessibility and mobility work to curb greenhouse gas emissions, reduce commuting times, and utilize existing and proposed infrastructure. However, these outcomes are not sufficiently interconnected with affordable housing provision in the GTA to mitigate gentrification and displacement pressures. As higher-income individuals are attracted to amenity-rich areas like those that surround a train station, lower-income individuals may be financially excluded by not having housing affordable to them. To explore the relationship between TOD policies and the occurrence of gentrification and displacement, this Major Paper focuses on Scarborough’s three Stouffville Line GO Train stations. The Major Paper analyzes whether gentrification and displacement have occurred in the areas surrounding these stations from 2016 to the present.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".