Bibliographic record
Abstract
Toronto’s waterfront is undergoing a large-scale redevelopment effort led by the arm's length public corporation Waterfront Toronto. Since 2005, Waterfront Toronto has operated a design review panel as a discretionary planning implementation tool. Case studies of design review in Canada and abroad have shown that design review can be an effective tool to improve design quality, but some development actors maintain the process faces challenges of clarity, consistency, and justification. These challenges and the recent demands from politicians to increase transparency of Waterfront Toronto’s decision-making helped form the overarching research question of this study: How can Waterfront Toronto’s design review panel (WDRP) advice become more transparent? Primary and secondary sources were used throughout this research project to provide a descriptive account of the level of WDRP transparency. I found that generally, but with some notable exceptions, the panel does operate transparently. Means for improving the process through monitoring and evaluation are theoretically feasible, but the realities of resource allocation are likely to be a constraint and therefore only incremental, informal monitoring may continue.
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 | 0.422 | 0.467 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".