Welcome to the [Growth] Machine: An Analysis of Heritage Conservation in the Intensifying City
Bibliographic record
Abstract
As municipalities strive to meet urban intensification targets, developable and redevelopable land has become increasingly scarce. As a result, inter-urban space is consistently under pressure to accommodate new uses. Consequently, properties of built heritage significance can become targeted for such accommodations, via three main intervention types: adaptive reuse, façadism, and demolition. Increasing the complexity of intervention type usage is that heritage valuation is fluid and differs between all actors, adding a social component to decision-making processes. Using the City of Toronto as the case and employing a mixed-methods study design, I sought to address three main inquiries. First, I analyzed how often adaptive reuse, façadism, and demolition are employed, and how their uses correlate with urban intensification. Second, I examined how heritage professionals value heritage conservation, how their valuation has changed over time, and their observations towards how other heritage professionals’ valuations have changed over time. Third, I sought to determine how heritage valuation translates into the preference of different intervention types across differing intensification scenarios. Preliminary, archival analysis showed that the number of heritage intervention projects has increased alongside intensification and that façadism has emerged as the most used intervention type. Subsequent social analysis revealed that all heritage professionals value conservation and that many professionals have experienced some level of valuation change throughout their career. Additionally, data revealed that level of experience and professional role/affiliation within the heritage planning domain played the greatest role in determining intervention type preference.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".