Vol. 18(1)- Replication Data for: Messi, Ronaldo, and the Politics of Celebrity Elections: Voting For the Best Soccer Player in the World
Bibliographic record
Abstract
It is widely assumed that celebrities are imbued with political capital and the power to move opinion. To understand the sources of that capital in the specific domain of sports celebrity, we investigate the popularity of global soccer superstars. Specifically, we examine players’ success in the Ballon d’Or – the most high profile contest to select the world’s best player. Based on historical election results as well as an original survey of soccer fans, we find that certain kinds of players are significantly more likely to win the Ballon d’Or. Moreover, we detect an increasing concentration of votes on these kinds of players over time, suggesting a clear and growing hierarchy in the competition for soccer celebrity. Further analyses of support for the world’s two best players in 2016 (Lionel Messi and Cristiano Ronaldo) show that, if properly adapted, political science concepts like partisanship have conceptual and empirical leverage in ostensibly non-political contests.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.094 | 0.109 |
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".