The restorative power of wounded healers in the (de)radicalisation context
Bibliographic record
Abstract
According to the Rome Memorandum on Good Practices for the Rehabilitation and Reintegration of Violent Extremist Offenders (2012) ‘reformed extremists, particularly those who have been through the rehabilitation process themselves, may be influential with inmates participating in these programs’. This important recommendation in de-radicalisation literature recognises the potential positive contribution a former radicalised individual can make to the reintegration process of others less far along in the process of de-radicalisation. In a inform session we shall conduct a theoretical, creative reflection exercise about how mentoring offered as a service by wounded healers (in this case former radicalised individuals that have undergone a process of de-radicalisation) could help in their own re-entry into the community upon release from imprisonment, and, simultaneously, how it could help community to overcome fear regarding the person re-entering and to restore trust on him/her. Applying a restorative lens when reflecting about the reintegration process of a former radicalised individual supports ‘that ‘giving back’ (would be) the right thing to do for victims and for society’ (Maruna, 2016: 295; Bazemore, 1998) and the mentoring activity of the wounded healer would provide the means to put into practice, to the extent possible, this reparation of the community he/she is returning to. In this context, mentoring would work as a restorative focused practice. The chance to witness the contribution the wounded healer is giving to peacemaking efforts should increase the community’s feelings of safety and empowerment, creating the conditions for the community to publicly recognise their re-established trust in the former radicalised person. In turn, this public validation should help the former radicalised individual to cement his/her new pro-social identity and, finally, experience the sense of fully belonging to the community. Indeed, grounded on the analysis of de-radicalisation literature, restorative justice literature and desistance of crime literature, we propose to creatively reflect on how the mentoring activities of a wounded healer could help in the process of (re)building identity and community in the context of de-radicalisation. We will explore how relevant inspiration could be extracted from restorative focused practices such as the evidence-based Canadian COSA Circles of Support and Accountability (Hannem, 2011; Wilson, McWhinnie & Wilson, 2008).
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".