Navigating Discourses of Racialized Bodies in Outdoor Leisure
Bibliographic record
Abstract
This chapter responds to Arai and Kivel’s (2009) call for a fourth wave of race research in leisure studies, emphasizing the need for critical reflection on how race and racialized bodies are shaped by white hegemony in outdoor leisure activities. The authors explore the entanglements of race, bodies, the outdoors and power dynamics through personal narratives from People of Colour, highlighting systemic barriers and lived experiences of exclusion. Drawing from leisure studies, Black geographies and critical race theory, this chapter critiques oppressive structures and calls for disassembling these and mobilizing social justice in outdoor leisure spaces. Personal accounts, such as those of Black, Iranian, Latina and Malaysian individuals, reveal the complexities of navigating predominantly white spaces, showcasing both challenges and potential for creating inclusive environments. The discussion extends to debunking myths surrounding race in leisure settings, equipping emerging professionals with strategies to address racial inequalities. Ultimately, the chapter aims to propel outdoor leisure practitioners towards radical change, encouraging justice, representation and equitable access in leisure spaces.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".