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Record W4409223805 · doi:10.1016/j.annale.2025.100175

When motivation follows the climate: Changing mountain environment influences motive constructs of recreational alpinists

2025· article· en· W4409223805 on OpenAlexaff
Emmanuel Salim, Célian Gruet, Philipp Sacher, Brooklyn Rushton, Katherine Hanly

Bibliographic record

VenueAnnals of Tourism Research Empirical Insights · 2025
Typearticle
Languageen
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsUniversity of CalgaryWilfrid Laurier University
FundersUniversité de Lausanne
KeywordsRecreationPsychologyClimate changeSocial psychologyIntrinsic motivationPolitical scienceGeology

Abstract

fetched live from OpenAlex

Alpinism, a high-risk and skill-intensive form of mountain tourism, is deeply intertwined with the mountain environment, making it a practice highly susceptible to the impacts of climate change. This study examines European alpinists' motivations and the impact of climate-driven landscape changes, like glacier retreat and increased hazards. Using a mixed-methods approach, the study combines a survey of 1071 alpinists with 30 in-depth interviews to identify key motivational constructs and how a changing climate influences these constructs. Results reveal that motives centre on the natural environment, social connection, the sporting nature of alpinism, and achieving iconic climbing objectives. The analysis highlights the emergence of a “last-chance to climb” motive, driven by disappearing or increasingly inaccessible routes due to climate change. • Mixed-methods explore climate change impacts on mountain climbers' motivations. • Alpinists are motivated by the environment, sharing experiences, sport and goals. • Four alpinist segments are identified based on their motives. • A ‘last chance to climb’ motive emerges among core climbers as routes disappear. • Risk perception, landscape loss, and route changes influences alpinist motives.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.210
GPT teacher head0.462
Teacher spread0.252 · 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 designTheoretical or conceptual
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

Citations1
Published2025
Admission routes1
Has abstractyes

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