Exploring The Nexus Between Heritage And Sustainability: How Business Improvement Areas (BIAs) Can Contribute To The Process
Bibliographic record
Abstract
Toronto has been undergoing rapid growth and development, dramatically changing the skyline of this city. Although this growth is exciting, it plays a hand in the threat against some of Toronto’s oldest buildings, sitting on prime real estate seen as ripe for redevelopment. Toronto needs to be more assertive when protecting its heritage assets, but has become largely reactive rather than proactive due to an overburdened Heritage Preservation Service department. The system needs to provide for more vigilance over threats to heritage and increase public awareness regarding the many benefits to protecting heritage properties. This paper explores how Business Improvement Associations can take on this role and stimulate the conversation of heritage conservation with property owners, developers, and other stakeholders, providing the support and vision over alternatives to demolition. This report also looks at the potential role the private sector has in heritage conservation, seeing it not as a barrier to development but something that needs to be commemorated because it is what makes Toronto unique.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".