Preventing Radicalization in Prisons: A comparative analysis of the Danish and Swedish Prison and Probation Service's counter-radicalization strategies within prisons
Bibliographic record
Abstract
It is important to direct resources to effective evidence- and value-based counter-radicalization strategies, especially within prisons. With the increasing threat ofviolence and terror from various violent extremist groups, such as Daesh, thefocus on prisons should be intensified. While radicalization within prisons was nota significant problem a decade ago, the new recruitment strategies from e.g.Daesh now pose a new challenge for prisons. This thesis identifies contemporaryliterature on radicalization, deradicalization, and violent extremism within aprison context. From this seven recurrent themes were identified: overcrowding,religious chaplains, sectioning, risk assessment, monitoring and supervision,rehabilitation programs and education of staff. This was then used to develop anassessment model, which was based on the Canadian Risk-Need-Responsivitymodel’s three core principles, for evaluating and conducting counterradicalizationstrategies. The assessment model was then used as a framework fora comparative analysis of the Danish and Swedish Prison and Probation Services’counter-radicalization strategies within prisons. The results show that bothcountries adhered to a degree to the assessment model and current literaturewithin the field. The results further reveal that there is a lack of empiricalevidence and data on radicalization and counter-radicalization within prisons, andthat the data available is somewhat outdated.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".