Designing Playful Urban Installations: An Exploration of Participatory Methods
Bibliographic record
Abstract
For decades the human dimension has been neglected in urban planning topics. With the emergence of modernism, streets started to get larger in order to accommodate more cars, and step by step, the space for pedestrians has been reduced, dictated by the rhythm of the automobile flow. With the invasion of cars into cities, the high-rise buildings and towers made cities less and less pleasant for inhabitants. In return, city life studies demonstrate where conditions for pedestrians are improved, social and recreational activities increase extensively. In light of this situation, a number of cities have integrated playful urban installations to revitalize city centers. In this research, the creative practice of designing four concepts of interactive urban installations through a research through design (RtD) approach combined with a reflective practice is described. Then since incorporating the notion of play in a way that encourages social interactions requires a good understanding of human behavior, site observations of two urban installations were also conducted in Montreal. Ultimately, recurring events and themes were investigated and turned into co-design activities for establishing participatory workshops. This process proved to be particularly useful for validating the developed concepts with potential users and created the ground for further reflection. Through this exploration, the core concepts of participatory practices were addressed which reconcile with the current endeavor for transforming situations to make cities more enjoyable and welcoming in the future.
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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 | 0.074 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".