The Social Mediatization of Lifestyle Sport: Continuity and Novelty in the Online Skate Subculture
Bibliographic record
Abstract
Based on mediatization theory, this article tracks how skateboarders experience and negotiate the entry of social media into their subculture. Building on existing scholarship, I show how social media and digital devices retain existing values within the culture while simultaneously introducing new challenges. To illustrate the phenomena of continuity and novelty in the online skate subculture, I analyze two case studies pertaining to YouTube. The first is a textual analysis of a typical skate video. Released on YouTube in 2020, the BE FREE video exhibits neoliberal, apolitical, masculine, and individualist values that go back decades in skate culture. The second case involves one of the most popular hubs of online skateboarding today: The Berrics YouTube channel, which claims 1.3 million subscribers and over 4,500 individual videos. I show how The Berrics maintains a one-dimensional positivity through its posts and interactions with fans, and I argue that it is still experimenting with the handling of negative feedback that participatory media allow. I also provide a brief history of skateboard media to properly contextualize these case studies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".