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 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".