A Cross-Sectional, Survey-Based Study of Equity, Diversity, and Inclusion in the Canadian Indoor Climbing Community
Bibliographic record
Abstract
Purpose: This study sought to offer insights into the demographics of the Canadian climbing community, as well as the perceived motivators and constraints to participating in climbing through an equity, diversity, and inclusion (EDI) lens. Approach: This cross-sectional, survey-based study was conducted in partnership with Climbing Escalade Canada (CEC), the national governing body of climbing in Canada. Findings: The average respondent in this study was white, heterosexual, young, highly educated and living in a household that earns over $100,000 annually. Social motivations were noted as a significant motivator for climbers—especially for women. Women, gender minorities, and racialized people all faced heightened constraints to participate in climbing. Implications: The findings of this study provide valuable insights for program and policy improvement across the Canadian climbing community, which can lead to sustaining the rapid rise in popularity taking place in the sport Research Contributions: With the exception of one recent study, much of the research investigating EDI in climbing has focused almost exclusively on gender and has been conducted outside of Canada. Future work within the sport of climbing can focus on improving the accessibility to climbing, as well as the overall sense of inclusion and diversity within the sport.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".