The sonic spectrums of skateboarding: from polarity to plurality
Bibliographic record
Abstract
The sounds produced by skateboards, or skatesounds, are a common basis of complaint among the urban public and yet a source of inspiration and joy for skateboarding participants. These opposing responses to skatesound have escaped scholarly attention due to skateboarding’s visuocentric culture, yet this disagreement is significant in planning for city-built skateparks, registering public complaints of skateboarders in city spaces, and adding hostile architecture like skate stoppers, which often pivot on this polarity of reactions to skatesounds. We present a spectrum of theoretical responses of skatesound to dispel these reactions, including subjectivism, semiotics, soundscapes, and texturology. We argue that for some people skatesounds may be merely subjective with either a positive or negative valence. For others, skatesound is associated with pro-social or anti-social behaviors. For some, skatesound is both associative and provides wayfinding information about a city. Lastly, we introduce a novel theory of texturology: that skateboarders possess a unique sensory knowledge of the surface materials and textures of the city through skatesound, a knowledge specific to skateboarding.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".