MétaCan
Menu
Back to cohort
Record W3036109124 · doi:10.1177/1012690220931736

“Helmets aren’t cool”: Surfers’ perceptions and attitudes towards protective headgear

2020· article· en· W3036109124 on OpenAlexaffabout
Nikolaus A. Dean, Andrea Bundon

Bibliographic record

VenueInternational Review for the Sociology of Sport · 2020
Typearticle
Languageen
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsParticipant observationPerceptionEthnographyPsychologyDentistryMedicineSociologySocial science

Abstract

fetched live from OpenAlex

Studies indicate that head injuries account for a significant proportion of all surf-related injuries. Yet, despite these rates, the vast majority of surfers do not wear protective headgear. Noting both the high rate of head-related injuries and surfers’ reluctance to wear protective headgear, this sociological study sought to critically explore surfers’ perceptions and attitudes towards protective headgear, and specifically to explore why so few surfers wear protective headgear. To address these aims, the ethnographic techniques of participant observations and qualitative interviews were used. In total, 12 experienced surfers from the West Coast of Canada were interviewed and over 30 hours of participant observations were collected. The findings illustrate that surfers do not wear protective headgear for four main reasons: (1) due to the idea that protective headgear is uncomfortable and could hinder performance; (2) due to the perception that protective headgear is only for other surfers; (3) based on the belief that surfing is not a high-risk sport; and (4) because of aesthetic reasons and/or the appearance of protective headgear. Using the concepts of subcultural capital and edgework, the study demonstrates how larger socio (sub)cultural factors linked to risk, control, and status influence and underline the surfers’ rationalizations for not wearing protective headgear.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score1.000

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)0.0010.000

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 head0.069
GPT teacher head0.388
Teacher spread0.318 · 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.

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

Citations7
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueInternational Review for the Sociology of SportSame topicAdventure Sports and Sensation SeekingFrench-language works237,207