Bibliographic record
Abstract
Electronic MonitoringSummaryThe main reason for bringing up the idea of electronic monitoring program is not only the bill submitted by a group of members of parliament, but also its advantages for offenders and the society.The concept of electronic monitoring of offenders, first conceived by an American psychologist, Dr. Robert Schweitzgebel in the 1960s, has been developed and implemented in many countries (USA, Canada, the United Kingdom, Australia, New Zealand, Singapore, South Africa, Sweden and Holland.) Programs based on electronic monitoring provide offenders with a more human contact and give opportunities for rehabilitation and reintegration. Electronic monitoring can be used on a number of offenders and suspect groups and situations, including pre-trial defendants, defendants on a conditional release and convicts on probation, parole or house arrest. Electronic monitoring also seems to be an efficient way to keep the budgets under control.The article presents the main problems connected with the idea of electronic monitoring, such as: technical and criminological aspects, aspects related to human rights - the right to privacy, the right to equality - influence on the offender’s family, chances to avoid negative consequences of incarceration. It is certain that bringing electronic monitoring program into effect in Poland should be preceded by a thorough analysis of programs already introduced in other countries - that is why the article tries to compare and contrast programs effective in some of the countries (United Kingdom, Germany, United States, Australia). Furthermore the article presents opinions on the electronic monitoring expressed by Polish probation officers and penal judges as well as their hopes and anxieties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.275 | 0.208 |
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".