Lessons From Critical Race Theory: Outdoor Experiential Education and Whiteness in Kinesiology
Bibliographic record
Abstract
Background: Outdoor experiential education (OEE) is often presented as a neutral and equitable curricular practice with positive learning outcomes. However, few studies have examined the experiences of racialized and queer White settler students or the representation of Whiteness in OEE curricular documents. Purpose: This article explores Whiteness, racialization, and Indigenous erasure in OEE as an undergraduate curricular practice at a Kinesiology program in a Canadian university. Methodology/Approach: Using critical race theory, a critical discourse analysis of six types of documents used to advertise and organize the outdoor experiential courses was combined with five semi-structured interviews with undergraduate students. Findings/Conclusions: This study demonstrates that students must negotiate Whiteness and settler colonialism to participate in OEE. Three main findings include the following: (a) The imagined student is wealthy and White, (b) students both assimilate to and resist codes of Whiteness, and (c) curricular documents and practices promote Eurocentricity and erase Indigeneity. Implications: OEE presents an opportunity for students preparing to become workers and educators in sport and recreation to learn about Whiteness, racialization, and Indigeneity. Kinesiology program design can use student narratives to shift from supposedly neutral curricular documents and pedagogies to ones that expose and work toward dismantling Eurocentricity.
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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 | 0.020 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.092 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".