The Role of File Character in the Implementation of the Principle of Proportionality of Punishment to the Crime during the Trial Phase
Bibliographic record
Abstract
The principle of proportionality of punishment to the crime represents completion of crime law for determining appropriate penalty, that is resulted from revolution of criminal justice in the way to considering the subject of criminal act and his personality. In fact, the correct response to against breaking norms needs to consider the question that "who did it" in spite of considering the act itself, it means that "what did the criminal do" or "what happened" by criminal prosecution authorities and specially courts and this time not only in terms of assign a criminal act to a person but also this means that what is the defendant's mental, physical structure and family history which could effect on his/her criminal characteristic is a necessary fact. File character that the necessity of formation was considered more in twentieth century, is called to a case that is formed besides file character and indicates information about general and special statues of criminal such as mental, family, educational and social condition. This file guide decision-makers and criminal justice to choose appropriate clinical methods and they will make appropriate criminal measures according to related information such as laboratory and conducted research in order to fulfill medical and correctness. In this research, we only study role of this file in trial phase.
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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 | 0.009 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".