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’.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".