Wybrane problemy profesjonalizacji organów probacyjnych i klasyfikacji sprawców oddanych pod dozór do grup ryzyka
Bibliographic record
Abstract
Selected problems of professionalization of probation agencies and classification of supervised offenders into risk groupA large prison population and high costs of the prison service contribute to rising interest in problems concerning effective correctional interventions and reduction of reoffending. In recent years, efforts aiming at reducing reoffending have resulted in many countries in significant reforms of national probation systems. Professionalization of probation agencies in order to improve their effectiveness has focused on two processes: risk assessment as well as implementation of interventions targeting dynamic risk factors risk management. In Poland, the requirement to assess the offenders’ risk of reoffending was introduced in 2013 by the order of the Minister of Justice. The purpose of this regulation was to adjust — at least to aminimal extent — Polish probation officers’ activities to model of functioning of probation agencies in the United States, Canada and Western Europe, as well as to recommendations included in the European probation rules. Unlike these countries, the probation service in Poland does not have structured risk assessment tools. The order issued by the Minister of Justice provides for only the classification of supervised offenders into risk groups based on arbitrary, rigid and controversial criteria which has little in common with building the probation service focused on risk management in the meaning of current European standards.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".