Recognising ‘community’ in condo law and living: hard lessons and soft learning from Melbourne and Toronto
Bibliographic record
Abstract
Purpose Ontario (Canada) and Victoria (Australia) are internationally recognised for best practice in Multi-Owned Property (MOP) living and law. Yet both jurisdictions struggle with the emerging urbanism associated with condominium MOP. This article aims to advance best practice by gaining insights into key MOP issues and challenges facing policy-makers and communities in Toronto and Melbourne. Design/methodology/approach Differential ways of recognising community in regulating and resolving challenging issues attending MOP urbanism will be examined typologically against public policy and political theory perspectives on community and collaborative approaches to social sustainability. A rich mixed-data analysis is used to develop a typology around three pillars of MOP community governance: harmonious high-rise living, residential-neighbourhood interface, and metropolitan community engagement. The article interrogates Canadian policy and law reform documents engaging Ontarian residents, and Australian disp...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".