A Comparative Study of Reformation and Treatment Strategies in the Stage of Implementing Imprisonment in Iran and Canada
Bibliographic record
Abstract
Investigating strategic thoughts of lawmakers and criminal policy-decision makers in the field of reformation and treatment in the stage of implementing imprisonment and the effort to make them applicable is an important matter that can represent a system’s purposefulness and program-oriented in the form of comparative study. In discussion of reformation and treatment if we attend to categorize strategies, it appears that alongside the basic and fundamental the strategies, some of the cases in the stage of implementing retribution, are supposed to be practical strategies. In this phase, establishing reformation-oriented and treatment-oriented courses, evaluating the effectiveness, priority in reformation and changing and specializing reformation are considered prominent. Paying profound attention to practical functions of reformatting mechanisms that being used in the stage of implementing imprisonment in Iran and Canada indicates that rather on first strategy that had given the most effort, in other cases Iran’s reformation system, unlike Canada’s, has weaknesses and lack of attention. Evaluation strategies, evaluating the effectiveness and specialization of reformation are major the strategies that ignoring them affects other strategies and thus reformatting foundations will be unsecured and they will be the cause for provided reformatting policies to be ineffective.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| 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".