Exploring the education background for children and young people sentenced to custody in England and Wales
Bibliographic record
Abstract
Crime and offending by children and young people has received attention in political, academic and media rhetoric for decades. Concerns over the offending behaviour of children and young people in adolescence, resulted in the Government developing strategies (for example, Transforming Youth Custody – Putting education at the heart of detention) to reduce the offending and re-offending of children and young people. The Transforming Youth Custody report found 50 per cent of children and young people aged 15-17 years-old were entering custody with literacy levels the equivalent of children aged 7-11 years-old. From the children and young people entering custody, 18 per cent had statements of special educational needs and 36 per cent of young men ceased attending school at 14 years-old. Figures from the Transforming Youth Custody report, support research conducted by Bryan (2004) that children and young people aged between 18 and 21 years-old in YOI has a high proportion of language difficulties. Such concerns highlight the importance of exploring the educational background of children and young people entering custody and the importance of addressing educational issues before children and young people enter custody. By completed a retrospective case file analysis on a representative cohort of children and young people in custody, the researcher will explore the educational background of children and young people entering custody in the United Kingdom. Findings from this analysis are complemented by a questionnaire implemented to discover children and young people’s views of. By exploring the educational background of children and young people in the United Kingdom, this research will highlight the role of education in promoting desistance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".