A Bio-Social Review to Mitigate the Punishment of Unwanted Acts
Bibliographic record
Abstract
Commitment of crime and exhibition of antisocial behavior have been considered as negative acts from early times of human civilization. Recent scientific advances have identified contributions of biological and sociological (environmental factors) factors in forming a maladaptive behavior. Generally, it is accepted by many scholars that punishing a wrongdoer, who has committed a crime owing to genetic predispositions and environmental elements, is not effective and forms of treatments should be replaced to avoid repeating a crime. Moreover, by identifying genetic deficiencies in an individual, an antisocial behavior could be potentially predicted and prevented before it comes to pass. On a whole, genetic and environmental factors, sometimes solely and some other times collaboratively, lead a person to act against society norms. In summary, this body of literature offers examples that explain factors which contribute to committing crimes and approaches which inhibit antisocial behavior. With regard to these aims, we suggest that punishment of criminals who are predisposed genetically in the same manner as other delinquencies is not justifiable and a reduction of punishment should be applied to such individuals. Moreover, by eliminating each of negative elements which contribute to antisocial behavior or crime, we can be more certain that the offender will not repeat antisocial acts after being released.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".