When celebrity athletes are ‘social movement entrepreneurs’: A study of the role of elite runners in run-for-peace events in post-conflict Kenya in 2008
Bibliographic record
Abstract
This paper reports findings from a study of the role played by high-profile Kenyan runners in the organization of Run-for-Peace events that took place in response to election-related violence in Kenya in late 2007 and early 2008. Acknowledging concerns expressed by some sociologists of sport about the role of celebrity athletes in the sport for development and peace movement, we suggest that in the particular contexts we studied, high-profile athletes played a crucial role in the organization of reconciliation events. Informed by interviews with former and current elite Kenyan runners and others involved in the organization of these events, we argue that the apparent effectiveness of the athletes in mobilizing resources, pursuing political opportunities and devising a collective action frame was possible because of the extant positioning of the athletes in the impacted communities, the active involvement in and personal investment of the athletes in the outcome of the peace-promoting activities, and the unique pre-Olympic moment in which the events took place. In doing so, we differentiate between celebrity athletes who are a ‘presence’ at sport for development and peace events, and those who might be considered ‘social movement entrepreneurs’. We conclude the paper by describing how strands of social movement theory were helpful in guiding our analysis of high-profile athletes and peace promotion, and with suggestions for future research pertaining to sport-related reconciliation movements.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".