The implications of condominium neighbourhoods for long-term urban revitalisation
Bibliographic record
Abstract
Condominium neighbourhoods are emerging in intensifying city centres as a response to market and demographic preferences for homeownership. While the multi-ownership structure of individual condominium buildings accommodates a short-term demand, the long-term implications for neighbourhoods is a source of concern. In particular, the presence of unit owners with varied acquisition objectives can lead to an anticommons problem resulting in building disinvestment due to an inability to reach decisions on sustainable maintenance fees and capital reserve funds, and a lack of end of lifecycle planning. The City of Toronto is experiencing unprecedented condominium development and serves as the basis for a case study that assesses the anticipated future neighbourhood challenges associated with a predominantly condominium-based form of ownership. Twenty-two local stakeholders were interviewed to identify problems that are viewed as sources of concern due to decisions made during the early stages of a building's lifecycle and the absence of a neighbourhood planning strategy. An analysis of the results indicates that lock-in, lacunae and neighbourhood effects will likely complicate revitalisation efforts as condominium neighbourhoods become more prevalent. Limited stakeholder recognition further suggests that it is necessary to raise a greater awareness of the potential anticommons impediments to long-term collective revitalisation actions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".