Climate impacts in sport: extreme heat as a climate hazard and adaptation options
Bibliographic record
Abstract
Rationale The aim of this paper is to present research examining how the climate hazard of extreme heat impacts varsity-level sport athletes and facilities, current responses, and options for adaptation.Methods A sample of 30 participants from a higher education institution athletics department was used with a two-phase Delphi study method that applied two iterations of questionnaires and mixed method analysis. The institution was situated in a region with a Köppen classification of “Warm Summer Continental Climate”.Findings Heat hazards aligned primarily with slow-onset, rather than fast-onset, climate impact categories. Adapting to heat hazards aligned with incremental adaptation rather than transformative adaptation. These findings suggest climate adaptation is a new concept for university sport and so is at a pioneering stage of practice.Practical implications Identifies options for sport managers for integrating adaptation into the strategic and operational thinking of sport organizations.Research contribution This paper extends knowledge by presenting evidence of heat risks to the sport as perceived by sport managers and participants during an era of climate change. The results address gaps in the existing literature by using primary source data to add to the evidence base for sport and climate change, and by identifying options for climate adaptation.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".