Modernity and the Question of the Existence of Moral Probabilities
Bibliographic record
Abstract
This short article discusses the relationship between law and morality in the modern era and proposes the use of a moral algorithm in litigation. The authors note that the main difficulty of legal consciousness is to determine the concept of law and the essence of justice, and that the coexistence of law and morality is where the contradictions of law are most clearly revealed. The research has two main goals: to follow traditional approaches to legal issues in relation to modernity, underlining existing and probable challenges, and to promote mechanisms regulating both moral and legal issues by developing a moral algorithm model. The authors acknowledge difficulties, since values are subjective, historically variable and shaped by cultural differences, and they point to a gap between what should be and what is, which they see as demonstrated by the Russia-Ukraine war. The proposed moral algorithm, a “questionnaire of morality”, would have two objectives: to help reveal the inner moral nature of a person and to help the judge make fair decisions, for example in limiting a person's freedom, keeping punishment and damages proportionate, and managing the offender's resocialization. The authors conclude that allowing the moral algorithm in court practice may be beneficial, while acknowledging that it requires serious research and analysis and cannot remove the risks of injustice. Abstract prepared by the editorial office from the full text of the article; the authors did not supply one.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".