Bibliographic record
Abstract
This paper takes up the question of whether the consequences of a person’s volitional actions can contribute to their blameworthiness. On the one hand it is intuitively plausible to hold that if A volitionally shoots V with the intention of killing V then A is blameworthy for V’s death. On the other hand, if the only difference between A and B is resultant luck, many find it counter-intuitive to hold that A is more blameworthy than B. There are three broad (non-skeptical) strategies for resolving this tension: accept resultant moral luck, deny that one can be morally responsible for outcomes, or accept that outcomes can be within the scope of things one is morally responsible for while denying that they can affect the degree of blameworthiness. This paper aims to defend resultant moral luck against both the scoping and the internalist strategies by drawing on an “inclusive conception” of blameworthiness, according to which how much blame one deserves is a function of two independent variables: the wrongfulness of the offense and the offender’s degree of moral responsibility. The view defended here holds that consequences affect degree of blameworthiness by affecting the wrongfulness of that for which one is being blamed.
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 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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".