parkrun across the pond: examining location and event characteristics in Canada and the United States of America
Bibliographic record
Abstract
Mass participation events, such as ‘parkrun’, may be one option to encourage physical activity within communities. The purpose of the study was to describe the expansion of parkrun locations in Canada and the United States of America (USA) and identify and describe characteristics of parkrun locations. For each parkrun site, setting-level information was collected from selected websites. Findings revealed many parkrun events were started in 2019 with most locations offering a virtual option during the COVID-19 pandemic. Further, parkrun events appear to be supported in cities/towns that vary in demographics. Two-thirds of USA parkrun cities were above the national average for individuals being within a short walking distance to a park; however, most parkrun locations are car reliant. There is room to expand parkrun where no events currently exist and additional research is needed to determine the degree to which area-level characteristics are associated with actual parkrun participation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".