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Record W4206585904 · doi:10.1080/13683500.2021.2023480

Climate change and the future of the Olympic Winter Games: athlete and coach perspectives

2022· article· en· W4206585904 on OpenAlexaff
Daniel Scott, Natalie Knowles, Siyao Ma, Michelle Rutty, Robert Steiger

Bibliographic record

VenueCurrent Issues in Tourism · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsClimate changeGeographyAdvertisingBusinessGeology

Abstract

fetched live from OpenAlex

The International Olympic Committee recognizes the risks climate change pose to the Games and its responsibility to lead on climate action. Winter is changing at the past Olympic Winter Games (OWG) locations and an important perspective to understand climate change risk is that of the athletes who put themselves at risk during these mega-sport events. A survey of 339 elite athletes and coaches from 20 countries was used to define fair and safe conditions for snow sports competitions. The frequency of unfair-unsafe conditions has increased over the last 50 years across the 21 OWG host locations. The probability of unfair-unsafe conditions increases under all future climate change scenarios. In a low emission scenario aligned to the Paris Climate Agreement, the number of climate reliable hosts remains almost unchanged throughout the twenty-first century (nine in mid-century, eight in late century). The geography of the OWG changes radically if global emissions remain on the trajectory of the last two decades, leaving only one reliable host city by the end of the century. Athletes expressed trepidation over the future of their sport and the need for the sporting world to be a powerful force to inspire and accelerate climate action.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.857
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.032
GPT teacher head0.329
Teacher spread0.297 · 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 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

Citations50
Published2022
Admission routes1
Has abstractyes

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