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Record W6902935166 · doi:10.7939/r3-vcs4-a174

Skiing Racialized Geographies

2022· dissertation· en· W6902935166 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2022
Typedissertation
Languageen
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousDiversity (politics)RecreationLegislationEthnic groupRacismColonialismRace (biology)

Abstract

fetched live from OpenAlex

“Skiing Racialized Geographies” examines how Black and Indigenous peoples are excluded in snow sports and how this lack of diversity can be addressed. Snow sports is a CAD $56.4 billion dollar industry, and the snow sports industry acknowledges that the lack of diversity contributes to industry stagnation. This is in spite of decades long efforts from both Black and Indigenous organizations working to foster participation. The snow sports industry has subsequently employed both the leisure theory marginality and ethnicity theses to explain the lack of racialized participation. The marginality thesis argues that racialized participation in leisure activities is statistically lower because of the inaccessible cost of participation and the unavailability of suitable facilities. The ethnicity thesis argues that racialized peoples have fundamentally different interests in terms of sport, leisure, and tourism, to explain lack of diversified participation in specific activities. Guided by Critical Race Theory (CRT) and Critical Indigenous Theory (CIT), this thesis first explores how colour-evasive and power-evasive logics, which ignore the underlying history and social construction of space, result in the lack of diversity in snow sports. This thesis explores the initial construction of outdoor recreational spaces, where snow sports take place, as spaces reserved for whiteness by starting with the history of the preservationist movement prior to 1930 and legislation enacted to create the first North American national parks. I then interrogate the history and social construction of outdoor recreational spaces through the lens of CIT to examine how the power-evasive colonial logics maintain imbalanced societal power relationships to justify settler occupation of space. Finally, I employ CRT to expose the colour-evasive neutral standard of whiteness and ontological individualism that falsely asserts that outdoor space is available to everyone equally while absolving dominant society of the responsibility of racialized inequity. Second, guided by the ethic of Indigenous storywork, this thesis examines how outdoor recreational spaces might be reconstructed in ways meaningful to racialized individuals to precipitate their participation in snow sports. This examination was undertaken as a qualitative research creation project using an original podcast to gather and share insights from 12 experts in the snow sports community, racialized as non-white, from Canada and the US. I analyzed the podcast transcripts using a thematic analysis which revealed six emergent themes: community engagement, education, leadership, and secret handshakes, barrier reductions, representation, Indigenous relationship with land, and common grounds. My findings show that the marginality and ethnicity theses insufficiently explain the lack of diversity in snow sports. Instead, the podcast data indicates that the exclusionary culture of the snow sports industry is responsible for the lack of diversity in participation. My research can be used by communities and industry because it points to appropriate solutions to consider when working to increase participation in snow sports in racialized communities. Finally, this study contributes to scholarly debates on resisting damage-centred research, critical geography, meaningful community engagement and representation, and research creation and podcasting as methodology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.009
Scholarly communication0.0030.003
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.008
GPT teacher head0.235
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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