Bibliographic record
Abstract
The speed and intensity of the current residential high-rise development of Toronto’s downtown core is unprecedented in North America. While this new population has significantly rejuvenated the street life and economy of this area, the public space to support it remains severely inadequate in quality, extent, and connectivity. This design proposal looks at the opportunities available to extract new public domain from existing remnant infrastructure within the historic core. By analyzing the urban design history of this area, and current pedestrian and traffic patterns, a new intervention is proposed that re-allocates space from cars to people. This proposal creates new amenity space and links a number of vestigial and derelict historic public squares into a larger comprehensive system. The proposed linear park establishes a needed cultural, civic, and community focus and encourages walking and cycling for this live-work population. The design is made present by a hyper-articulation of its horizontal surface—a repetitive and continuous paving with a memorable pattern and form. This strategy is a pragmatic but aggressive approach that recognizes the criticality of extracting a pedestrian realm within the emerging hyper-density of this particular car-oriented city. It makes use of available historic patterns, and existing redundant traffic infrastructure, to create place, meaning, and amenity.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".