Neighbourhood Resilience Planning with Renters in Hamilton, Ontario, Canada
Bibliographic record
Abstract
This sequential mixed-methods study demonstrates the co-production of knowledge between academic researchers and local communities. Guided by the resilience planning framework, the study engaged 48 renters from the Beasley neighbourhood of Hamilton, Ontario, Canada in identifying stressful and supportive locations using a participatory mapping tool called the “Place Report.” A total of 74 Place Reports were submitted, with most reporting supportive places in or near Beasley. Thematic analysis revealed three overarching domains, and 13 themes related to the use and function of these locations. Places pertaining to food, housing, and physical activity emerged as the most frequently discussed topics. The findings, which also included suggested interventions and local issues, were presented at a resilience planning meeting attended by participatory mapping participants and other community members. This meeting facilitated contextualization of the mapping data and generated further discussion on relevant local issues and potential interventions. Although limitations were noted regarding sample representativeness and participatory mapping tool validity, the study contributed meaningful, community-driven insights for neighbourhood resilience planning within Beasley. The findings are intended to inform local policymakers and community leaders to help address stressors and promote well-being among Beasley residents.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".