Bibliographic record
Abstract
In the course of the research, the author discovered 31 criminal cases considered by Russian courts resulting in a non-rehabilitating decision due to causing death when providing first aid. At the same time, this phenomenon is mostly unknown to domestic researchers. The purpose of the paper is the criminal law assessment of the first aid provision by non-professional subjects. To achieve this goal, the following tasks were set: to establish the possibility of the presence of an extreme necessity in the provision of first aid; to establish conditions under which causing harm in the provision of such assistance does not entail criminal liability; to make proposals for eliminating defects in law enforcement and improving Russian legislation. To achieve these tasks, the author applied formal legal, formal dogmatic and statistical methods, as well as a set of general philosophical methods, including analysis, synthesis, deduction and induction. As a result of the study, cases of inconsistency of judicial practice with the norms of the domestic criminal law on extreme necessity were identified. Contrary to the arguments of some courts, when harm is caused during first aid, there may be some extreme necessity, exceeding which does not entail criminal liability for causing harm. The conclusions of the study are that the Russian criminal law on extreme necessity corresponds to the tasks facing the legislator; the problem lies in the insufficient understanding of its provisions by the judges. The author made proposals for reforming related provisions of the criminal law on the basis of the legislation of Canada and the United States.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".