Youth Justice and Education: A Typology of Educational Approaches to the Resocialisation of Young Offenders in Spain
Bibliographic record
Abstract
The Young Offenders Act that entered Spanish law in 2000 represents a significant attempt to place personalised educational programmes for young people with social/criminal problems at the centre of policy and practice. This paper examines the teams and educational programmes designed to manage and implement the goal of ‘re-socialisation’ enshrined in the Act. This paper focuses on an analytical typology of the educational styles used at young offenders institutions in Spain. The analysis highlights the differences between styles in relation to the key underlying objectives of social reorientation and citizen empowerment. The wide range of models experimented with in different Spanish regions has great relevance to youth justice systems in other countries. For example, the typology we put forward has concrete implications for recent developments in England, in the light of moves towards establishing a national network of “Secure Colleges” for convicted young people.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".