“I don’t want to get in anyone’s way”: mapping girl skateboarders’ navigation of place and power in skate spaces
Bibliographic record
Abstract
Skateboarding is an informal activity with a relatively low cost of entry, and a range of potential practice grounds. Without formal gatekeepers, it is potentially inclusive on social, economic, and cultural levels. Participation has increased overall in recent years, including by girls and young women, who are increasingly visible in skateboarding organisations, international competition, and media. However, skateboarding spaces remain dominated by white, middle-class, male participants. Why, then, is the increased diversity of participation not diversifying the wider culture of the sport? Furthermore, skateboarding research has not been methodologically innovative, limiting its potential to see what is happening. This paper charts the development of a mapping tool as part of a wider study of young woman skateboarders, designed to better understand how different skateboarders (and others) use, move, and interact within skateboarding spaces. Drawing on behavioural mapping frameworks, we show how our mapping tool developed into a comprehensive, transferable system through which complex, fast moving, leisure settings can be studied. We conclude that being in a space does not always confer full access to participate in it. Examples from our research and consultancy demonstrate how the system can be used to illuminate power relations and interactions within active leisure settings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".