The Slayer Rule in the System of Legal Sanctions for Murder (on the Example of Anglo-Saxon Countries)
Bibliographic record
Abstract
The article deals with issues related to the slayer rule in the framework of the common law system using the example of the USA, the UK, Canada, Australia and New Zealand. The slayer rule does not allow a person who killed or contributed to the death of another person to benefit from the murder. The positions reflected in judicial practice, as well as the legislative consolidation of the slayer rule, are analyzed. The slayer rule is a sanction on the borderline between civil and criminal law. Three principles according to which the slayer rule is applied are the intention of the testator, equity and morality. The application of the slayer rule is decided in accordance with established judicial practice and the discretion of the court. The authors discuss the application of the slayer rule in cases of manslaughter and murder committed in the situations of domestic violence. Some aspects of applying the slayer rule to murderers with mental disorders are considered. In some US states, the slayer rule applies not only in the case of murder, but also in the case of abuse of helpless elderly people for financial profit. The authors analyze judicial precedents and changes that affected the slayer rule throughout its existence.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".