Piloting the Use of Concept Mapping to Engage Geographic Communities for Stress and Resilience Planning in Toronto, Ontario, Canada
Bibliographic record
Abstract
The physical and social characteristics of urban neighborhoods engender unique stressors and assets, contributing to community-level variation in health over the lifecourse. Actors such as city planners and community organizations can help strengthen resilience in places where chronic stress is endemic, by learning about perceived stressors and assets from neighborhood users themselves (residents, workers, business owners). This study piloted a methodology to identify Toronto neighborhoods experiencing chronic stress and to engage them to identify neighborhood stressors, assets, and solutions. Crescent Town was identified as one neighborhood of interest based on relatively high levels of emotional stress in Twitter Tweets produced over two one-year periods (2013–2014 and 2017–2018) and triangulation using other neighborhood-level data. Using concept mapping, community members (n = 23) created a ten-cluster concept map describing neighborhood stressors and assets, and identified two potential strategies, a Crescent Town Residents’ Association and a community fair to promote neighborhood resources and build social networks. We discuss how this knowledge has circulated through the City of Toronto and community-level organizations to date, and lessons for improving this methodology.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".