“We are Still Running around with the Same Rules, but We are Not the Same We Were 20 Years Ago” – Exploring the Perceptions of Youth Justice Professionals on Secure Training Centres
Bibliographic record
Abstract
Debates on the incarceration of children in residential settings has been ongoing for decades, with the United Nations Convention of the Rights of the Child and academic literature acknowledging that custody is not in the best interest of the child. In England, the problems associated with placing children in custody have been documents since 1999 and, nearly twenty years later, a BBC Panorama exposed the abuse of children at the hands of staff in the same Secure Training Center. This paper examines staff’ and other professional perceptions as to the purpose and direction of Secure Training Centers, youth custodial environments, through thematic analysis of semi-structured interviews with staff members employed in Secure Training Centers and other professionals in the youth justice sector (i.e. Social Workers, Youth Offending Officers and Managers). It seeks to identify perceptions on the purpose and challenge of Secure Training Centers in supporting children who have experiences adverse circumstances resulting in trauma. It illustrates the need for embedding trauma-informed “Child First” approaches in Secure Training Centers, and youth custodial environments globally, to enable staff to adequately support children to build empowering relationships.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.023 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".