The Role of Condominium Amenities in Community Building
Bibliographic record
Abstract
Many Canadian cities are facing densification through the condominium-boom. Planning policies and neoliberalism are encouraging this form of housing. The term “community” is recognized in legal and land-use planning processes through a political lens, but it does not consider the sociological aspect. Residents make the community by developing relationships. Research is needed to identify if residents enjoy their condo amenities and if they feel it has an impact on community building. By researching this matter, planners, policy makers and condo board members can make certain changes that may improve the residents’ sense of community. This study consists of a mixed methods approach: quantitative and qualitative. Data on condo amenities has been collected from a real estate website, for data on an inner city (Downtown Toronto), an older suburb (Scarborough) and a new suburb (Vaughan). This provides data on the types of amenities available to condo residents. Residents from these areas also described their experiences. Both of these methods inform the condo residents’ perspectives. As there is a rise of feeling lonely in today’s society, cities need to plan for the psychological wellbeing of inhabitants. The narratives of cities are changing, which means that definitions of community are also changing. It is important to make structures that satisfy the psychological and physical needs of residents.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".