Once a Criminal, Always a Criminal? Attitudes Towards Reintegration of Released Prisoners Among Israeli Public
Bibliographic record
Abstract
The purpose of this study is to examine the attitudes of different groups toward the reintegration of released prisoners in Israeli society, characterized by the groups’ ability to directly or indirectly contribute to the prisoners’ reintegration in normative society. The sample included 551 subjects divided into three groups: Representatives of the law enforcement system, owners or managers of businesses of different sizes, and members of the general public. A mapping sentence, defining a specific world of content, was defined for each of the three groups. Using this mapping sentence, the researchers constructed a separate questionnaire for each test group, phrased according to Likert scale. The findings of this study suggest that, at declarative level, a high percentage of the subjects express sympathetic attitudes towards the reintegration of released prisoners in the community, and their rehabilitation within it. However, it should be noted that there is often a gap between a person’s stated position and his/her actual behavior. We can conclude that members of the Israeli public do not declaratively express an extreme position against reintegration of released prisoners. The findings of this study suggest that it would be beneficial to increase public awareness in Israel of the advantages of rehabilitating and reintegrating released prisoners in the community.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".