Technosexuality from the Perspective of Forensic Medicine, Imamiyya Jurisprudence, and Iranian Criminal Law
Bibliographic record
Abstract
Technosexism is a general term for sexual attraction to machines, robots, and androids, and in its specific sense, robot fetishism is a sexual orientation to humanoid robots or a sexual orientation to people who act like robots.Some moralists consider sex robots to have some functions of respecting the freedom of choice of the individual; a suitable alternative to marriage for people who are unable to form a family and treating some sexually transmitted diseases, but most of them believe that due to more negative effects, including; an instrumental view of women, a weakening of the concept of man and human relationships; a lack of sense of responsibility resulting from forming a family; along with legal morality and religious considerations, they are considered an immoral option.Their users often have physical, sexual, or psychological problems, and are likely to commit crimes such as rape, child abuse, and sexual violence due to unrealistic persuasion, increased feelings of loneliness, facilitating fantasies of sexual assault, and the implementation of pornographic fantasies. The health status of these individuals will be examined by a forensic physician to determine the level of criminal responsibility. Imami jurisprudence considers the use of sex robots to be forbidden and considered masturbation, but Iran's criminal law has remained silent on this issue. The legal systems of Canada and the United States, following the criminalization of the import of such robots, have prevented their promotion and use except in essential cases, such as helping to treat patients with disabilities or sexual defects. Therefore, it is appropriate that transparency and criminalization be implemented in the Iranian criminal system as well.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".