Restorative justice and mental illness : combating the "spider syndrome"
Bibliographic record
Abstract
This study explores the experiences and perspectives of three restorative justice (RJ) practitioners who processed cases involving service users with serious and persistent mental illness. Participants were recruited in the United Kingdom with the help of restorative justice contacts in London, England. The three practitioners who came forward took part in one semi-structured interview that lasted approximately one hour. A descriptive framework was used and emerging themes were coded. Research findings show that personal contact along with mental health education appeared to increase participants’ willingness to process RJ cases with service users who have serious and persistent mental illness, due to a subsequent reduction in stigmatizing thoughts and behaviours. The latter are components of what one of the participants referred to as the “Spider Syndrome”: fears and misconceptions that occur due to a lack of knowledge and understanding. This, along with participants’ own recommendations, suggests that mental health training which focuses specifically on skills and raising awareness, in combination with increased exposure to individuals who have serious mental illness, could be an important contributor to enhancing practitioner skill and increasing the use of RJ with such service users. Findings also suggest that restorative justice can be used as a stigma-reducing tool, both at the practitioner and service user level. These insights may hold important implications for social work, mental health and development of practice in the field of restorative justice.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".