Utilising Urban Gamification for Sustainable Crime Prevention in Public Spaces: A Citizen Participation Model for Designing Against Vandalism
Bibliographic record
Abstract
In order to create secured urban spaces, public safety need to be considered as the duty of citizens as well as official authorities. Therefore, this research focuses on the social environment of public spaces and how to encourage citizens to take prompt actions to detect, report and deter any illegal activities. Moreover, graffiti is considered as the most common type of vandalism worldwide that threatens not only our public and private properties, but also our social environment. In order to resolve the problem of graffiti, this research examines current citizen participation model applied by different stakeholders in Fukuoka City in Japan. Current model has been illustrated based on several in-depth interviews conducted with different stakeholders and citizens in Fukuoka City. Then, a new model has been proposed based on urban gamification to encourage more citizens to act as passive observers in public spaces. Proposed model has been evaluated by local communities and city hall to understand its potentials. This research found out that proposed model has the potentials to encourage more citizens to be part of the solution by being more active in public spaces. However, few obstacles regarding budget and administration might stand in the way of achieving such a concept.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".