Skateparks as a health‐resource: are they as dangerous as they look?
Bibliographic record
Abstract
Skateboarding raises an interesting dilemma in the field of health promotion. While public health institutions are engaged in unprecedented efforts to counter the sedentary lifestyles of youth, the promotion of lifestyle sports and active leisure practices, such as skateboarding, is tempered by the potential risks of injuries. The health‐risks associated with skateboarding have generally been viewed through the lens of epidemiology. Sociology, on the other hand, has yet to provide research on injuries that meshes with this literature. This paper addresses this absence by drawing on the works of Pierre Bourdieu to present a different perspective on the health benefits and injuries associated with skateparks. Using his concepts of ‘physical capital’ and ‘social capital’, the analysis consists of 388 days of observation (mean of 35 days in 11 skateparks) and 23 in‐depth interviews with male and female skaters in Montreal, Canada. Its findings indicate that few serious injuries occurred in these skateparks and that these spaces do not correspond to the image depicted of skateboarding in previous research. From this, we suggest that skateparks should be conceived as a valuable health‐resource for youth because they provide various social, psychological and physical resources that encourage a safe and active lifestyle.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 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".