Citizen Voice in Decision-Making: Exploring the power of dialogue in Toronto’s public spaces
Bibliographic record
Abstract
Public spaces are the lifeblood of a community, offering opportunities for socialization and community activities, facilitating the flow of movement across the city, and inspiring a sense of place and belonging. UNESCO (2017) refers to public space as “an area or place that is open and accessible to all peoples, regardless of gender, race, ethnicity, age, or socio-economic level”. It is this fundamental communal aspect that sets public spaces apart and makes them a vital part of the city. \n \nCitizens stand to benefit the most from public spaces as extensions of their own living rooms outside the home. However, citizens possess little power to influence the planning and design processes. \n \nThe system map aims to visualise the complex realities of the stakeholder and influences that are involved in Toronto’s public spaces. This map explores how citizen dialogue can influence the social future of city streets and public spaces. The map takes the viewer on a journey that informs the influences the current system borrows from the past, observes the present situation, power dynamics and the actors who wield this power, and still envisions a better tomorrow. This system of urban spaces and city streets is layered and nuanced, with many factors, histories, and challenges, and there are many systemic barriers and delays that stand in the way of change. \n \nIt offers small-scale innovations which have the power to grow and eventually become embedded into the culture and processes of the overall system. It is our firm belief that individuals and small groups of people with the right tools can be the leverage points themselves for systemic change in their streets, neighbourhoods, communities, and cities. \n \nCitizen dialogue is a powerful tool for the human-centred design, strategy, and planning of public spaces and will greatly influence the social future of a city’s human capital and quality of life. Cities are at the heart of many challenges we face today, but the solutions to these problems also lie at the heart of the cities.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.032 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".