Imputability, answerability, and the epistemic condition on moral and legal culpability
Bibliographic record
Abstract
Abstract This paper has two main goals. The first is to defend a particular account of answerability according to which a person is (morally or criminally) answerable for their conduct if it is (morally or criminally) wrongful under the same description under which it is imputable to their agency. Negating defences in law aim to defeat criminal answerability by negating some element of the charged offence while their moral analogues aim to defeat moral answerability by defeating the aptness of the description under which an action is imputed. In contrast, affirmative defences and their moral analogues aim to defeat the move from answerability to liability to sanction by offering an exculpatory explanation for the wrongful conduct. The second goal of this paper is to argue that there are important differences between the way that ignorance functions in negating defences and their moral analogues and affirmative defences and their moral analogues. Specifically, when ignorance functions to defeat answerability, it need only be sincere, but it is typically limited to matters of fact, whereas both moral and non‐moral ignorance can excuse one from liability but only if non‐culpable.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.024 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".