Alternative Media in Alternative Sport: Platforming Working Conditions in Professional Skateboarding
Bibliographic record
Abstract
Alternative media enable marginalized people to voice their experiences, challenge dominant ideologies, and circumvent mainstream gatekeepers. Podcasts are an alternative medium that can be counterhegemonic, foregrounding such issues as antiracism, Indigeneity, LGBTQ rights, socialism, and workers’ perspectives. This article expands on alternative-media research by transporting it to the skateboarding subculture. I first depict the skateboard outlets Thrasher Magazine (1981) and The Berrics (2007) website as hegemonic and mainstream. By contrast, I depict podcasts The Bunt (2016) and Vent City (2019) as counterhegemonic and alternative. I then ask: To what degree do skate podcasts acknowledge professional skateboarders as workers? And: Do such shows allow skaters to express grievances with their industry? A discourse analysis of Thrasher and The Berrics demonstrates that they often mystify freelance work, class, and skaters’ working conditions. An analysis of The Bunt and Vent City suggests that podcasts offer unique and radical perspectives, though attention to working conditions is uneven. I find there may be too much overlap between the case studies for an alternative/mainstream distinction to be meaningful. Political currents within skateboarding are still promising, however, and digital media will be essential in making the subculture and industry more inclusive.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".