Challenges and Legal Solutions of Electronic Monitoring-Based Incarceration: A Comparative Study of Iran and Leading Countries
Bibliographic record
Abstract
Leading countries in the field of electronic monitoring-based incarceration, such as France, Canada, and the United States, have effectively utilized this approach through comprehensive legislation and the adoption of advanced technologies. This study, conducted using a descriptive-analytical method, presents a comparative analysis of Iran and leading countries and offers recommendations for drafting comprehensive laws, developing infrastructures, and fostering public awareness. The findings indicate that the successful implementation of this method depends on integrating electronic monitoring with principles derived from situational crime prevention theories, deterrence justice, social control, and rational choice theory. For Iran, adopting these approaches can significantly enhance criminal policies and mitigate implementation challenges. The experience of France demonstrates that electronic monitoring can serve as an effective tool in reducing prison overcrowding, facilitating rehabilitation, and enhancing social security. Related legal frameworks, such as Loi Perben II and Article 723-7 of the French Code of Criminal Procedure, have clearly established the necessary legal foundations for leveraging the benefits of this system. This experience can serve as a model for other countries, particularly Iran, to improve judicial efficiency through the adoption of this method. Additionally, similar to France’s 2019 Domestic Violence Prevention Law, Iran can utilize electronic monitoring to restrict offenders' contact with victims and enhance victim safety. By enacting comprehensive legislation, developing technological infrastructures, and integrating electronic monitoring with rehabilitation programs, Iran can effectively employ this approach within its criminal justice system. A comparative analysis of the United States, Canada, and the United Kingdom, alongside Iran’s existing challenges, further reveals that public awareness campaigns and specialized workforce training are also essential for the successful implementation of this system in Iran
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".