Bibliographic record
Abstract
Electronic Monitoring historically got started with the aims not only to solve the problem of over population in prison and save the cost of managing prison but also to help offenders to reintegrate into society through treatment inside society in America such as U.S and Canada and now it is used with various functions in each countries. In case of South Korea, Electronic Monitoring was recently adopted under crisis of many sex offending happened cross the country. Electronic Monitoring in Korea has both functions of enforcement against sex offender which means recidivism and reintegration into society. However, there are several problems in terms of cost efficiency, its effectiveness and infringe on human right by over surveillance and regulations. Also, there have been yet enough empirical and demonstrative researches regarding its efficiency and effectiveness. Therefore, this research analyzes the cost efficiency and its effectiveness of Electronic Monitoring by looking into the cases of foreign countries which have already managed Electronic Monitoring. After analyzing, it introduces two alternatives of adopting treatment model which is consisted of Medical and Psychological treatment. When it comes to Psychological treatment, it introduces Self control treatment and Environmental effect treatment. Finally, this research introduces the way of effectiveness of Electronic Monitoring in goal, function of detention, restriction and surveillance, and its object.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".