Understanding Newcomer Challenges and Opportunities to Accessing Nature and Greenspace in Riverdale, Hamilton, Ontario: A Neighborhood-Centered Photovoice Study
Bibliographic record
Abstract
BackgroundAccess to and engagement with greenspace is related to improved health benefits. We sought to collaborate with community members as partners in research and co-creators in knowledge to better understand which components within a newcomer-dense community help or hinder individual and community efforts to access greenspace and nature-based activities.MethodsWe used photovoice methodology to engage with local residents in focus groups, photowalks, and photo-elicitation interviews. Themes were developed using direct content analysis.ResultsA total of 39 participants (ages 11-70 years; median years in Canada of 3.25 years) were engaged in this program of research. From the analysis, we developed four themes: (a) peace and beauty; (b) memories of home; (c) safety and cleanliness; and (d) welcoming strengthened and new opportunities. Participants associated nature with peace, citing it as "under-rated" but "vital" to the neighborhood. Via photographs and stories, participants also shared a multitude of safety concerns that prevent their access to green/outdoor spaces for healthy active living programs or activities (e.g., woodchip-covered playgrounds, ample amounts of garbage littering the park and school grounds, lack of timely ice removal on sidewalks, limited safe biking paths, and unsafe motor vehicle practices at the crosswalks surrounding local parks).ConclusionTo translate the key ideas and themes into an informed discussion with policy and decision-makers, we held an in-person exhibition and guided tour where community members, the lead photovoice researcher, and SCORE! principal investigator shared information about each theme in the form of a pseudo-narrative peppered with prepared discussion questions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".