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Record W4414103366 · doi:10.1080/14927713.2025.2539699

Pratiquer les sports de nature estivaux en moyenne montagne. Modalités d’adaptation et d’atténuation face au changement climatique

2025· article· fr· W4414103366 on OpenAlexvenueno aff
Anne-Sophie Crépeau, Perrin-Malterre Clémence, Ba Moussa-Mamadou

Bibliographic record

VenueLeisure/Loisir · 2025
Typearticle
Languagefr
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsFace (sociological concept)Government (linguistics)Context (archaeology)Work (physics)Order (exchange)

Abstract

fetched live from OpenAlex

Peu d’études s’intéressent aux conséquences du changement climatique sur les pratiques estivales de sports de montagne. Pourtant, les impacts sont perceptibles et contribuent à changer les pratiques face aux aléas climatiques. Cet article vise à déterminer les facteurs qui influencent les pratiquants de sports de nature à s’adapter au changement climatique et contribuer à son atténuation. Pour répondre à cette question, une enquéte quantitative par questionnaire fut administrée auprès de 1 510 pratiquants de sports de nature en moyenne montagne, suivi de 25 entretiens semi-directifs. Les résultats montrent que le changement climatique impacte leur pratique puisque 86 % des répondants déclarent recourir à des actions d’adaptation et/ou d’atténuation. Plusieurs facteurs permettent d’expliquer l’adoption de ces comportements. Les femmes ont davantage tendance à les adopter. Le nombre d’activités pratiquées, le niveau de connexion à la nature et la conscience de l’origine anthropique du changement climatique sont également des facteurs favorisant les actions d’atténuation.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.015
GPT teacher head0.316
Teacher spread0.301 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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