On the Necessity of Referral to t he Category "Randomity" In the Process of Moral Assessment of Individual's Acts
Bibliographic record
Abstract
In the article, firstly through the analysis of the history of ethical teachings, the basic requirements for the procedure of moral assessment, for its subject (the legitimacy of this assessment) and the object (taking into account the consequences of the act, their motives, as well as the ratio of the goal and the means used to achieve it) are identified. Consideration of a particular deed taking into account all these aspects, it would seem, allows us to make its moral assessment objective, impartial, fair. However, secondly (and most importantly!), we substantiate the thesis that if this consideration is not supplemented with the category of randomity (moreover, interpreted as aclinamen), then the resulting assessment will itself be morally problematic, giving rise to moral complacency and depriving us the opportunity to help a morally stumbled person.
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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 | 0.042 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.090 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.011 |
| 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".