Is There a Normatively Distinctive Concept of Cheating in Sport (or Anywhere Else)?
Bibliographic record
Abstract
This paper argues that for the purposes of any sort of serious discussion about immoral conduct in sport very little is illuminated by claiming that the conduct in question is cheating. In fact, describing some behavior as cheating is typically little more than expressing strong, but thoroughly vague and imprecise, moral disapproval or condemnation of another person or institution about a wide and ill-defined range of improper advantage-seeking behavior. Such expressions of disapproval fail to distinguish cheating from many other types of immoral conduct. The discussion shows that we should set the concept aside and assess the moral disapproval implied by claims of cheating by reference to the moral and other principles that underlie the practice of sport. This allows us to consider carefully the complexity of the issues that are raised when allegations of cheating are made and not be distracted by the emotionally loaded, conversation-stopping tendency of the concept. This means that some types of disputes in sport will be messy and demand more effort to resolve, but the payoff will be better informed and more thoughtful discussions and greater awareness of the moral complexity of sport and of its principled underpinnings.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.050 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".