Placemaking & Engaging Diverse Communities: Exploring Opportunities for Community Arts in Toronto’s Mobility Hubs
Bibliographic record
Abstract
As high-volume nexuses between different modes of transportation interfacing with various other employment, retail, residential and recreational land-uses, mobility hubs are a key component of expected future improvements in Toronto’s transit infrastructure. Transit-oriented considerations have increasingly become a central factor guiding urban development and growth. The inherent challenge of engaging a diverse urban population in decisions about the built environment can be further compounded when seeking to animate communities with marginalized populations while using ‘one size fits all’ engagement methods. Nonetheless, the dynamic nature of sustainability creates a need to revise community visions frequently. This paper draws on interviews with community arts professionals and art educators, as well as representatives from transportation, urban planning, urban design and architecture to explore the potential of community art as a transformative tool and as a way of fostering more inclusive urban regeneration. The potential for community art as a more central element in the planning and development of mobility hubs is also examined. The results identified that community arts and arts-based engagement strategies have the potential to help overcome many of the pervasive barriers to participation associated with traditional engagement methods. A host of process- and outcome-oriented benefits were identified by participants, including the potential for fostering inter- and intra-neighbourhood dialogue, building a stronger sense of neighbourhood identity, and developing capacity towards community-led neighbourhood regeneration. The results have implications for the transformative and capacity-building potential of community art and arts-informed engagement strategies as perceived and utilized by urban planners.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".