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 of \nviolence and terror from various violent extremist groups, such as Daesh, the \nfocus on prisons should be intensified. While radicalization within prisons was not \na significant problem a decade ago, the new recruitment strategies from e.g. \nDaesh now pose a new challenge for prisons. This thesis identifies contemporary \nliterature on radicalization, deradicalization, and violent extremism within a \nprison context. From this seven recurrent themes were identified: overcrowding, \nreligious chaplains, sectioning, risk assessment, monitoring and supervision, \nrehabilitation programs and education of staff. This was then used to develop an \nassessment model, which was based on the Canadian Risk-Need-Responsivity \nmodel’s three core principles, for evaluating and conducting counterradicalization \nstrategies. The assessment model was then used as a framework for \na comparative analysis of the Danish and Swedish Prison and Probation Services’ \ncounter-radicalization strategies within prisons. The results show that both \ncountries adhered to a degree to the assessment model and current literature \nwithin the field. The results further reveal that there is a lack of empirical \nevidence and data on radicalization and counter-radicalization within prisons, and \nthat the data available is somewhat outdated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".