Attitudes of Sport Fans Toward the Electronic Sign-Stealing Scandal in Major League Baseball: Differing Associations With Perfectionism and Excellencism
Bibliographic record
Abstract
The winners of the 2017 World Series were found guilty of illegally using electronic devices to steal the signs of their opponents. Many but not all sport fans negatively reacted to this cheating incident. We relied on the model of excellencism and perfectionism to determine if perfection strivers are less unfavorable toward electronic sign stealing (cheating) compared with excellence strivers. Sport fans (N = 321) completed a measure of excellencism and perfectionism. We used three different approaches to measure attitudes toward electronic sign stealing in baseball. Results of a multivariate multiple regression showed that sport fans who are perfection strivers held more favorable attitudes toward electronic sign stealing compared with excellence strivers. Perfection strivers also reported higher moral disengagement and winning-at-all-cost mentality. These findings are insightful because they indicate that perfectionistic standards significantly relate to sport cheating-related attitudes once we separate excellencism from perfectionism.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| 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".