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Record W3201395102 · doi:10.1123/ssj.2020-0161

“A Lot of What We Ride Is Their Land”: White Settler Canadian Understandings of Mountain Biking, Indigeneity, and Recreational Colonialism

2021· article· en· W3201395102 on OpenAlexaffabout
John Reid-Hresko, Jeff R. Warren

Bibliographic record

VenueSociology of Sport Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsQuest University Canada
Fundersnot available
KeywordsRecreationIndigenousColonialismWhite (mutation)NegotiationSociologyIgnoranceCommissionNarrativeEnvironmental ethicsGeographyEthnologyGender studiesPolitical scienceLawSocial scienceArchaeologyEcology

Abstract

fetched live from OpenAlex

This article explores how White settler mountain bikers in British Columbia understand their relationship to recreational landscapes on unceded Indigenous territory. Using original qualitative research, the authors detail three rhetorical strategies settler Canadians employ to negotiate their place within geographies of belonging informed by Indigeneity and recreational colonialism: ignorance, ambivalence, and acknowledgement. In Canada’s post-Truth and Reconciliation Commission climate, the discourses settlers use to situate themselves vis-à-vis landscapes and Indigenous people contribute to the conditions of possibility for meaningful movement toward a more equitable existence for all. This work points to a growing need to problematize the seemingly apolitical landscapes of recreation as a prerequisite toward meaningful reconciliation.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.047
Threshold uncertainty score0.339

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0310.030
Scholarly communication0.0100.002
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.035
GPT teacher head0.317
Teacher spread0.282 · 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

Citations12
Published2021
Admission routes2
Has abstractyes

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