Reclaiming recreation: leveraging a participatory mapping approach for effective recreational PA advocacy
Bibliographic record
Abstract
This brief report presents a feminist participatory mapping initiative addressing inequities in recreational physical activity (PA) access within a social housing community in [municipality], Canada, with a focus on methodological innovation and advocacy outcomes. Using an intersectional geography lens, the project conceptualized ‘recreational PA deserts’ as areas where environmental, social, and policy barriers restrict access to health-promoting spaces. Conducted with [community-based organization], the initiative combined community move-throughs, sketch mapping, interviews, data scans, and digital cartography to identify barriers and co-create an advocacy story map. Embedded within a broader evaluation of trauma- and violence-informed physical activity programmes, the project emphasized community leadership throughout. Key findings highlighted poor infrastructure, safety concerns, and transportation barriers as major obstacles despite strong community interest in recreational PA. Outcomes included new funding for intergenerational programming and municipal investment in infrastructure. This brief paper contributes to leisure studies by demonstrating how participatory mapping can be used to identify recreational PA barriers and mobilize community-led advocacy for infrastructure and policy change.
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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 | 0.071 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.020 | 0.020 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".