Condos in the Suburb: What are the Drivers Behind the Decision to Move into Suburban Condominiums?
Bibliographic record
Abstract
High-rise residential condominiums are increasingly utilized by suburban municipalities in Canada as an alternative to suburban sprawl for accommodating population growth. Despite its increasing adoption, little research exists to support the effectiveness of this growth management tool, and a key indicator of its success is on whether its suburban condo dwellers exhibits a consumption pattern and lifestyle that are more urban in character. This study offers a comparative analysis on the lifestyles and motivations of suburban condominium dwellers, their respective municipalities, and condominiums clusters located in the downtowns of Toronto and Vancouver. It finds that the suburban ‘condo boom’ is fueled by a transient population characterized by tenuous socio-demographic status, childless and small households, and is currently not in a position to replace lower density forms of housing. In addition, the characteristics indicative of an urban form of living, namely that of reduced land consumption and auto-dependency, and increased levels of active transportation and public transit use, are either absent or only realized to a limited degree. The problems of how to ensure larger, relatively affordable condominium units, and how to enhance the transit-supportiveness of the suburban downtown are distilled from the findings as key issues to overcome, and five recommendations are made to address them.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".