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Record W4385876860 · doi:10.1080/17458927.2023.2245232

The sonic spectrums of skateboarding: from polarity to plurality

2023· article· en· W4385876860 on OpenAlexafffund
Brian Glenney, Max Boutin, Paul O’Connor

Bibliographic record

VenueThe Senses and Society · 2023
Typearticle
Languageen
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsUniversité du Québec à Montréal
FundersConcordia University
KeywordsSemioticsSubjectivismSoundscapeSociologyAestheticsPsychologyCognitive psychologyLinguisticsEpistemologyArtAcousticsSound (geography)Philosophy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.315
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2023
Admission routes2
Has abstractyes

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