Motives and barriers to climate activism by elite and professional athletes
Bibliographic record
Abstract
Research question What are the motives and barriers to climate activism for elite and professional athletes? How do these change over time?Research methods Semi-structured interviews were conducted with 27 elite (Olympic-level) and professional (paid) athletes from 7 countries and 15 sport who are climate activists. Over a three-year period (2021–2023), each athlete participated in 1–4 interviews.Results and findings One or more catalyzing events, a sense of urgency, and a self-ascribed sense of responsibility were the initial motivators for climate activism, while a sense of overwhelm and perceived lack of knowledge were noted as barriers. Over time, the eighteen athletes who participated in more than one interview had a different set of answers for what motivated them to continue: positive feedback and a strong sense of support, while a lack of support, the hypocrisy trap, low confidence, and time poverty were barriers.Implications This study extends the extant athlete activism literature by applying theories and categorizations of activism to a new subject of activism: climate activism. It also offers introduces professional athlete perspectives to the climate activism literature, and a longitudinal perspective on experiences of athlete activism by following the participants over a three-year period. Managerial implications for athletes, agents, team management, and athlete-focused nonprofits and charities are discussed.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".