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Record W2268144266 · doi:10.14288/1.0066436

Making meaning out of mountains : skiing, the environment and eco-politics

2008· article· en· W2268144266 on OpenAlexaboutno aff
Mark Christopher John Stoddart

Bibliographic record

VenuecIRcle (University of British Columbia) · 2008
Typearticle
Languageen
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsnot available
Fundersnot available
KeywordsMeaning (existential)PoliticsEnvironmental politicsPolitical scienceEnvironmental ethicsAestheticsGeographyEpistemologyLawArtPhilosophy

Abstract

fetched live from OpenAlex

This research provides a sociological analysis of skiing as a form of outdoor recreation and nature tourism in British Columbia, Canada. A qualitative multi-method approach is used, combining discourse analysis, interviews with skiers, and unobtrusive field observation at Whistler Blackcomb and Whitewater ski resorts. Through a focus on discourse, embodied interactions among humans and non-humans, and flows of power, this research describes an environmental ambiguity at the centre of skiing. There is a tension between interpretations of skiing as an environmentally-sustainable practice and notions of skiing as an environmental and social problem. Skiing is based on the symbolic consumption of nature and is understood by many participants as a way of entering into a meaningful relationship with the non-human environment. However, interpretations of skiing as a non-consumptive use of non-human nature are too simple. Social movement groups disrupt pro-environmental discourses of skiing by challenging the sport’s ecological and social legitimacy. Many skiers also articulate a self-reflexive environmental critique of their sport. In these instances, skiing is brought into the realm of politics. Recreational forms of interaction with the non-human environment tend to be at the periphery of environmental sociology. At the same time, sport sociologists tend to focus on the social dimensions of outdoor recreation, while bracketing out non-human nature. This research brings these two fields of inquiry into dialogue with each other, thereby addressing this double lacuna.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.223
Teacher spread0.198 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2008
Admission routes1
Has abstractyes

Explore more

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