Evaluating the Viability of Intensification Around Three Suburban GO Stations
Bibliographic record
Abstract
The updated Growth Plan for the Greater Golden Horseshoe (2017) requires all GO rail station areas to achieve a minimum density of 150 residents and jobs combined per hectare. Intensification is unlikely to occur without intentional policy and partnerships to encourage development. Mid-rise buildings, the typical residential form needed to meet the intensification target, are often challenging to develop in suburban areas. Similarly, employment intensification can be challenging to achieve without incentives for office location. Policy, politics and market viability all impact intensification. Changes are needed at the provincial, regional and local level to encourage development. Regulatory changes like zoning, partnerships between Metrolinx and developers and/or financial incentives, such as reduced development charges, should be explored to encourage intensification. Suburban GO rail station intensification is an opportunity to achieve multiple policy goals, such as the creation of walkable, affordable communities that increase housing choice. An article on transit station intensification in the Greater Toronto and Hamilton Area, used the keywords: transit oriented development, intensification, Greater Toronto and Hamilton Area
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".