Leading with Landscape: Enhancing the Process for Cultural Landscape Adaptive Reuse in Ontario
Bibliographic record
Abstract
Ontario’s cultural landscapes are evolving places facing challenges of growth and conservation. While other jurisdictions have moved toward more integrated approaches that center cultural landscape conservation within the broader spatial planning process, Ontario’s legislative framework and guidance can result in a siloed approach. The goal of this thesis is to critique the current process and suggest next steps for a holistic, integrated, and future-oriented process for the adaptive reuse of post-institutional cultural landscapes in Ontario. This will draw upon other Canadian and international landscape approaches that consider ecological, social, cultural and economic factors. This research uses mixed-methods including a literature scan, process mapping, an Ontario cultural landscape practitioner focus group, analysis, synthesis, and reflection. This research puts forward recommendations that build on current cultural landscape practice, which are intended to serve as a reference for practitioners in developing their own approaches to adaptive reuse projects that lead with landscape.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".