Smart Citizens Enabling Resilient Neighbourhoods (SCERN): Participatory mapping platform for resilience planning at a neighbourhood scale
Bibliographic record
Abstract
Urban communities face a range of stressors, including crime, pollution, and infrastructure challenges, which disproportionately affect marginalized populations. Resilience planning can help address these issues, but existing tools often lack meaningful community involvement. This paper introduces the Smart Citizens Enabling Resilient Neighbourhoods (SCERN) participatory mapping tool, a geo-questionnaire-based mobile GIS application designed to engage community members in resilience planning. SCERN facilitates data collection on local stressors and support systems through demographic profiling and spatial mapping, allowing for a nuanced understanding of place-based experiences. This tool was pilot tested at Wilfrid Laurier University in Waterloo Canada, with 33 participants in two groups submitting 113 place reports. Analysis of these reports identified key locations associated with spatial patterns of resilience as well as locations for targeted interventions. Using the tool in combination with a broader resilience planning framework ensures that community members are central to both data collection and planning processes. Furthermore, SCERN's adaptability renders it a valuable resource for urban planners, researchers, and community organizations. By fostering community participation, this tool provides a scalable and customizable approach to resilience planning that prioritizes equity and inclusion.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".