Bibliographic record
Abstract
Contemporary debates about "moral luck" were inaugurated by Thomas Nagel’s celebrated essay on the topic. Nagel notes that the puzzle about moral luck is formally parallel to the familiar epistemological problem of skepticism. In each case, the problem is generated by the apparent coherence of the thought that inner aspects of our lives are self-contained, and can be both understood and evaluated without any reference to anything external. Epistemological skepticism begins with the thought that my thoughts could be exactly as they are without any contact with the world outside, where "exactly as they are" is glossed in terms of the grounds that connect those thoughts with each other and provide the basis for our confidence in them. In the practical case, the problem of moral or legal luck arises from the thought that the only basis we have for evaluating a person’s action is his decision to perform that action. The Kantian and post-Kantian response to epistemological skepticism is not to try to defeat the skeptic on his or her own grounds, but rather to show that there is something wrong in the way the problem is set up. Our ordinary ways of thinking about ordinary things, and other persons, are only in trouble if they rest on an unwarranted inference from something that is more secure. I will engage with legal luck in a parallel way: our ordinary ways of thinking about responsibility are only in trouble if they rest on an unwarranted inference from something more secure. I argue that the concept of a completed wrong is basic to law, and that aspects of human interaction on which luck-skeptics focus — blameworthiness and harm — are derivative. I frame the issue not in terms of moral significance, but rather in terms of the authorization of the state to use force, either to order the payment of damages in tort or to imprison criminals. In the first part of the Article I consider the case of tort liability for negligence, explaining the structure of a completed wrong and the correspondingly derivative significance of carelessness as such. In the second part I turn to the issue of criminal punishment. I offer a brief explanation of the state’s authority to punish, before going on to show that the basic case that engages this authority is the completed crime. I then show why the failed attempt also attracts punishment, even though it is a derivative case.
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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.099 | 0.020 |
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".