Examining attitudes towards inclusion and social justice among U.S. Climbers: analysis and findings from a national survey
Bibliographic record
Abstract
Climbing in the U.S. resembles other ‘alternative’ and ‘lifestyle’ sports in that it has long been an exclusionary leisure pursuit. Whiteness, a history of land dispossession, and settler colonialism have reinforced exclusive boundaries to the sport through a ‘hierarchy of participation’. Formal moves from climbing advocacy organisations, gyms, and brands have increased diversity and justice efforts in climbing through funding, pledges, and social media messaging. However, these initiatives receive varied support from within climbing communities. This paper employs a social-psychological approach to examine which factors likely help shape and maintain variation in U.S. climbers’ concern for matters related to inclusion and justice in climbing. We draw data from a national survey conducted in collaboration with the national advocacy organisation Access Fund. We then apply multiple regression to examine how various climbing and demographic attributes are associated with inclusion and justice concerns by testing three hypotheses: experienced privilege and/or marginalisation, partisan political affiliation, and issue prominence. Our findings suggest that variation in climbers’ inclusion and justice attitudes appears driven more by respondents’ affiliation with one or more marginalised identities, political leanings, and other sociodemographic characteristics that we suggest are representative of the ‘politics of everyday life’.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".