“Helmets aren’t cool”: Surfers’ perceptions and attitudes towards protective headgear
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".