Promoting Adaptive Reuse in Ontario: A Planning Policy Tool for Making the Best of Manufacturing Decline
Bibliographic record
Abstract
The exodus of manufacturing jobs from industrialized cities has increasingly altered the way municipalities plan and cope with buildings and areas that once served as industrial and economic centres. Now these often derelict and costly structures sit as an eyesore in many communities which experience symptoms of post-industrialism. The practice of adaptive reuse is a unique concept of city building, where demolition and traditional brownfield redevelopment have been common practice. Though an already established method, adaptive reuse is becoming increasingly popular due to a greater intensity to protect heritage, reuse materials and structures, and offer unique architectural spaces, there has been a demand to reuse former industrial buildings for other uses such as commercial and recreational spaces. To achieve this, there must be sufficient policy in place to incentivize and mitigate the increase cost and risk which are usually associated with this type of development. This article will focus specifically on Ontario, Canada, and the current Official Plans of all 51 of the province’s cities, and how they are addressing adaptive reuse in former industrial areas and unique ways in which they address this problem. A content analysis of the documents showed that there is a wide difference in reuse contextualization and suggested policy directives. However, Cities in Ontario have proposed that affordable housing, intensification, revitalization in the urban core, and creating spaces for creative and vibrant industries can be addressed by the promotion of reuse in the community. For those with strong industrial history, the applicability of reuse allows for communities to preserve their industrial heritage, while at the same time shift uses to the new economy.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".