The Intrinsic Value of Co-Designing Skateparks
Bibliographic record
Abstract
The exclusion of skateboarders from skatepark planning, the rejection of skaters from public space and the lack of inclusive co-design methods leads to poorly designed and neglected skateparks. It is hypothesized that local skateboarders are the experts in creating sustainable skatepark design yet they are usually the last group to be consulted on these developments. Indeed, unlike every major city in Canada, Toronto does not even have a permanent indoor skatepark facility in the downtown core. After months of civil activism which prompted a city-wide Skatepark Study Report, The City of Toronto made a financial commitment in 2016 to address the need for an indoor skatepark. This emancipatory research study was created in response to that and uses co-design methods to explore the value of a DIY skatepark. Researchers engaged local skateboarders in conversations and activities around all aspects of skatepark creation. \nThe study aims to show that skaters are the best experts to consult regarding the design, development and ongoing maintenance of skateparks. This co-design framework encourages inclusive, sustainable design principles that incorporate creative and artistic skateable obstacles into skatepark design.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".