BUILDING TOGETHER: EMPOWERING COMMUNITIES TO CO-CREATE URBAN LIVING
Bibliographic record
Abstract
Co-creation is an opportunity to bring together the government, private sector, and community stakeholders in order to build more enjoyable and inclusive urban spaces in which to live, work and play. There are many cited benefits to inviting citizens and community members into the urban design process: for local government, it can be a way to collect community needs and ideas and manage risks more proactively; for private developers, it can allow them to tap directly into the market for new ideas; and for community members, it can provide them with a sense of belonging, representation and ownership by influencing the decisions that directly affect their health and wellbeing. \nDespite these benefits, co-creation of urban living spaces with the community is still widely viewed as a risky, emergent approach that in many cases is being practiced in a performative manner, or not at all. While major cities in Europe and Asia have begun to pave the way for successful approaches to this practice, North American cities have an opportunity to address the systemic barriers that currently limit more inclusive and equitable co-creation. \nThrough both secondary and primary research, this paper maps out the current models and frameworks of citizen co-creation in the context of urban planning, specifically focusing on the city of Toronto, Canada. We identify the barriers and limitations that may currently prevent equitable and inclusive participation from community stakeholders. Further, we propose a theory of change for how to address these barriers and disrupt negative feedback cycles, while also putting forth five actionable strategic interventions that will ideally help practitioners in the field contribute to enabling a shift towards more equitable and inclusive community participation in the urban planning ecosystem.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.037 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".