Experiential Density: Infill strategies within Toronto's Centres - Bringing Human Scale, Character, and Walkability
Bibliographic record
Abstract
A variety of approaches are being used to accommodate Toronto’s growing population. Most of these solutions rely on high-rise and mid-rise developments, emphasizing the quantity of density instead of the quality. However, this thesis focuses on blocks of slab towers, and explores how the perception of an environment and intensity of development can form an experiential density. Introducing new public and pedestrian orientated spaces to the neglected land between apartment towers to improve the experience of urban blocks also offers open space to increase the density in Toronto’s designated growth areas. To achieve this new environment, urban blocks containing clusters of slab towers will be fragmented into walkable distances; scaled outdoor spaces will activate the neglected park; infill will increase density and define outdoor spaces; and transitional areas will mediate the public and private realm, all to bring life into the block and improve experience of urban density.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".