‘Karma is the guy on the Chiefs … ’: contextualizing putting lipstick on a pigskin and the Taylor Swift and Travis Kelce romance
Bibliographic record
Abstract
In September 2023, global pop music icon, Taylor Swift, attended a Kansas City Chiefs National Football League (NFL) game, confirming rumors that she was dating player Travis Kelce. In this paper, I address a gap in the sport philosophy literature analyzing musicians and sport and the connections with American Football and gender tropes. After Swift’s attendance at the game, there was a 400% spike in Kelce jersey sales. Additionally, there was a 63% increase in women NFL viewers aged 18–49. I analyze the interconnections of Swift and football from a feminist lens. I make two claims in this paper: (i) The Swift-Kelce romance has overshadowed the NFL’s significant ethical problems, and (ii) Swift challenged stereotypical ideals and expectations of women and girlfriends of sports stars (WAGS). I will also refer to the extensive criticism and misogyny that Swift experienced while attending NFL games and highlight the political connections with gender and football.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.029 | 0.058 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".