Bibliographic record
Abstract
This study aims at investigating characteristics of prisoners who are guilty of crimes such as manslaughter and battery. 5700 Iranian prisoners are grouped based on their social statuses in terms of their educational level, sex ratio, marital status, and occupational status. In order to carry out the study, available documents and characteristics of prisoners are used. Analysis of the quantitative data indicates that characteristics of prisoners are congruent with their committed crimes. In fact, social statues of prisoners have serious influence on their behaviors or misbehaviors. As well, women hardly commit such crimes. The reasons behind this case may be due to their religion or culture. Iranian women do stick to their faith, religion and Islamic culture. Generally, a possible explanation for these crimes may be the lack of knowledge, cognizance or education. Education as a correctional system is the development of the abilities of the mind (learning to know and to control) and reduces crime rates. Crime is a part of human society; thus, finding solutions to its complete destruction is urgent. According to Lochner and Moretti (2003), although education raises some costs, it decreases crime rates, makes persons patient, and shows the better ways. Education also changes the minds, opens the eyes, and makes better lives. On the other hand, punishment scarcely acts as a deterrent. In summary, killing of a human being is forbidden (Haram) in Islam and saving one person means saving the whole society. But it is necessary to analyze characteristics of offenders to stop such crimes in the society. More importantly, art of research and development of related information decrease the crime rates in the country.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".