Climate change and the future of the Olympic Winter Games: athlete and coach perspectives
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".