Bibliographic record
Abstract
The revitalization of formerly dark, dirty and often uninviting urban spaces is occurring across many cities throughout North America. This is because the hundreds of kilometers of laneways located behind buildings to be viewed as significant semi-public spaces and are being redeveloped into active spaces that can play a role in improving the state of the natural environment. The City of Toronto has a vast laneway system that is not being utilized to its full potential. This report attempts to demonstrate this point and suggests that there is an opportunity for recreating these laneways into vibrant spaces that support the natural environment while maintaining their primary functions as light vehicular thoroughfares and access points for homes and businesses. Through the examination of nine laneway redevelopment programs and projects this report highlights the successful techniques being implemented within these laneways and emphasizes the significant lessons that can be learned. Finally, each lesson learned is review, and recommendations are given on how the City of Toronto can potentially address each point if attempting to implement its own laneway redevelopment program. Among a host of recommendations, this includes the need to promote laneway redevelopment through a change to the City's existing land use planning policies; the development of laneway design guidelines; and, the implementation of a dynamic funding system.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".