Gnarly Freelancers: Professional Skateboarders’ Labor and Social-Media Use in the Neoliberal Economy
Bibliographic record
Abstract
The working conditions of professional skateboarders are rarely investigated in academic literature or traditional skate media (e.g., Thrasher Magazine). This article contextualizes skateboarding labor and compares its professionals with other freelance contractors in the precarious neoliberal economy. It also explores the role of social media in skateboarders’ careers; while experiencing data mining and the fetishism of digital devices like any other online user, pro skaters must adopt platforms (e.g., YouTube) for their career advancement, as greater notoriety leads to corporate sponsorships. I outline the multiple hats that skaters wear, such as the sponsored athlete, the walking advertisement, and most importantly the emerging social-media adept. Within this context, the article further details the coercive forces keeping skaters amenable to sponsoring companies and industry insiders, such as the pejorative label of “kook.” Finally, I explain a contradiction that the profusion of Web 2.0 use has led to slight but not proportional coverage of skaters’ working conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".