Community Engagement, Collective Spaces, and Well-Being in Community Housing Neighborhoods: The Case of Foster Farm, Ottawa
Bibliographic record
Abstract
The relationship between architecture and well-being is a complex and evolving topic, yet often underutilized. This study delves into the relationship between architectural design, collective spaces, and the well-being of individuals within social and community housing neighborhoods. This research project is grounded on my experience volunteering at the Ottawa community of Foster Farm between 2014 and 2018. Through my involvement in the Junior Youth Spiritual Empowerment Program and other community-building activities at Foster Farm, I learned about the absence of suitable collective spaces in the area which caused significant challenges in sustaining regular community activities. Employing a mixed-methods approach, including first-hand experience, consultations with residents and social workers, and urban theories on design and well-being, this study identifies ways in which the neighborhoods can be more supportive of their resident’s needs and aspirations. The research also offers insights into effective and ethical ways to engage communities through service and listening.
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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 | 0.001 |
| Science and technology studies | 0.032 | 0.011 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".