Scales of ignorance: an ethical normative framework to account for relative risk of harm in sport categorization
Bibliographic record
Abstract
Sport categorization is often justified by benefits such as increased fairness or inclusion. Taking inspiration from John Rawls, Sigmund Loland’s fair equality of opportunity principle in sport (FEOPs) is a tool for determining whether the existence of an inequality ethically justifies the institution of a new category in any given sport. It is an elegant ethical normative framework, but since FEOPs does not account explicitly for athlete safety (i.e. athlete physical and mental wellbeing), we are left in an ethically dubious situation where the risk of harm associated with a categorization regime might in fact prove to be greater than the risk of harm present within the sport before its introduction. To address this critical gap, I propose the ‘scales of ignorance’ ethical normative framework to weigh the relative risk of harm within a sport, crucially inserting athlete safety into the discourse surrounding ethical justification for categorization in sport. The current paper is the first explicit formulation of assessment and ethical justification of risk of harm in the familiar logic of FEOPs. The scales of ignorance framework can also be used independently of Loland’s approach. Two new concepts are also proposed: ‘insidious risk of harm’ and ‘pernicious risk of harm’.
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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 | 0.047 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.055 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".