Female skateboarders and their negotiation of space and identity
Bibliographic record
Abstract
I have considered how female skateboarders negotiate space and identity in a male-dominated subculture and question a society that is intent on perpetuating an attitude that subcultures are a domain for developing masculinity. Female skateboarders have always participated alongside the men, but it has been a constant struggle because their accomplishments are rarely encouraged by the skateboard media industry, and mainstream media representations reflect this practice. In recent years, girl skaters have taken it upon themselves to create their own visibility and develop networks of support by producing websites, videos, zines, and organizing "all-girls" skateboard competitions. As a result, a unique community of female skateboarders has been established. Female skateboarders come from a variety of backgrounds, and I focus upon the experiences of a group of women skateboarders from Montreal, Canada by conducting interviews and observing how the group has evolved. Despite the diverse and even contradictory responses, it is apparent that when female skateboarders actively participate in the subculture they are redefining traditional notions of femininity and assumptions regarding who should be valued as an "authentic" skateboarder. My thesis traces a history of women in skateboarding, describes their activities as cultural producers, and offers insider accounts, such as acts of resistance. I also make comparisons with the situation of sportswomen and challenge past subcultural research that limit the participation of girls to their relationships with male members. It was my goal to exhibit how women can adopt, share and innovate their subcultures' ideals.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.022 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".