Planting Imagination: Community Co-Design for Toronto’s Chinatown West
Bibliographic record
Abstract
Planting Imagination ran from 2021 to 2023 (during a pandemic recovery period) in Toronto’s Chinatown neighbourhood. It brought together a group of local Chinatown community organizations and University researchers to recruit 60 diverse ‘Chinatown Activators’ (CAs) and six Community Facilitators (CFs) from across the community. CFs and Cas used virtual reality (VR) technology to co-design a local community garden and develop new visions for the future of Chinatown. Using cutting-edge VR visioning and the principles of the Collaborative Community Engagement Model (CCEM) co-design, the Chinatown community was provided with a platform to virtually envision the future of their own community and neighbourhood as a collaborative process. In doing so, they explored how we might transform the way we build and mobilize communities, (re)construct communityidentities, and strengthen the community’s resilience to promote social justice and equity. This process strengthened community solidarity to enable local residents to more readily steward the future of the built environment and respond collectively to challenging events like the pandemic. Bringing together diverse disciplines and practices (including architecture, cultural psychiatry, interior design, immersive technology, computer science and public health), Planting Imagination developed models of therapeutic VR co-creation delivered through a series of online and in-person multi- lingual community co-design and co-fabrication sessions that prioritized the communities and neighbourhoods disproportionately impacted by COVID-19.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".