Skirtboarder net-a-narratives: Young women creating their own skateboarding (re)presentations
Bibliographic record
Abstract
By creating and posting their own stories on the Internet, sportswomen are able to challenge the persistent, sexist, mainstream and alternative (skateboarding) media (re)presentations of female athletes. A Foucauldian discourse analysis of 262 posts of the Skirtboarders’ blog – a Montreal-based, Canadian female skateboarding crew’s Internet project – explores the ways in which a group of sportswomen circulate alternative discourses of femininity. In these (re)presentations, the Skirtboarders embrace various femininities and, at the same time, reject binaries (male/female) without explicitly claiming a feminist agenda or attaching themselves to other oppositional discourses. This indicates a third-wave feminist sensibility. The Skirtboarders reproduce some normative discursive fragments commonly found in media (re)presentations. Furthermore, they post links to mainstream and alternative media coverage of their crew, which at times reflects the ‘problems’ of historical media coverage (sexualization, marginalization and trivialization). However, most of their online productions portray them as polygendered skaters (action shots, skating activities and lifestyle) and are thus radically different. The Skirtboarders’ discursive portrayals of female skateboarders are therefore uniquely alternative to other media (re)presentations but at the same time, paradoxical.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".