Bibliographic record
Abstract
On Jeff McMahan's influential ‘responsibility account’ of moral liability to defensive killing, one can forfeit one's right not be killed by engaging in an ordinary, morally permissible risk‐imposing activity, such as driving a car. If, through no fault of hers, a driver's car veers out of control and toward a pedestrian, the account deems it no violation of the driver's right to save the pedestrian's life at the expense of the driver's life. Many critics reject the responsibility account on the grounds that, first, it has draconian implications for threateners like the driver, and second, it contravenes the plausible principle that wronging one's victim is necessary for forfeiting one's rights. But I argue, drawing on the account's luck‐egalitarian underpinnings, that (1) the account lacks the draconian implications widely attributed to it, and (2) contrary to what many assume, wrongdoing is unnecessary for rights‐forfeiture. Via these arguments, I seek both to deepen our understanding of the responsibility account, and to reissue it in a more plausible and attractive form.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| 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".