Bibliographic record
Abstract
In this chapter, I move on to resulting normative questions about the culpability principle, causal contribution, and command responsibility. I engage in deontic analysis of what type of contribution the culpability principle actually requires, and whether the requirement might be reconceived. First, I will examine why criminal law requires causal contribution, and the degree of contribution required. I will argue that the requirement in relation to accessories is not onerous; ‘risk aggravation’ satisfies the culpability principle. Second, I consider ambitious proposals to re-imagine culpability, i.e. to develop a new deontic account that does not require any causal contribution. A theme of this book is that ICL can present us with new questions that can lead us to adjust our basic assumptions from criminal law theory. On this issue, however, although the arguments are intriguing, my conclusion is that on a coherentist all-things-considered judgement, they are as yet far too tentative and undeveloped to provide a convincing basis for criminal sanctions. Accordingly, the current best theory is that accessories must at least elevate the risk of the crimes occurring.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".