Does prison education in Scottish young offenders’ institutions work?
Bibliographic record
Abstract
Compared with the rest of the UK, particularly England and Wales and indeed other countries such as Canada, Australia and USA, (see, for example, Esperian 2010; Baylis 2003; Tracy 2003; Morin 1981; Merrington et al. 2004; Jancic 1998; Lockwood et al. 2012; Darling and Price 2004), prison education in Scotland has not been subjected to much scholarly attention. This is despite the fact that Scotland spends a substantial amount of its prison budget on offender opportunities, including education and vocational skills training (Scottish Government 2014). Given the uniqueness of Scotland as a devolved region of the UK, with powers, inter alia, over the criminal justice system, including prisons (Audit Scotland 2005), offers particular insights regarding the nature of prison education (learning, vocational training and employment skills) for both male and female young offenders (16-21 years). There is need therefore to subject to scholarly investigation recent reports as to why there seems to be a sharp fall in the numbers of male young offenders reoffending after leaving prison (see Leask 2015). Areas of research interest are, but not limited to, the resources available, extent and quality of provision, inmates’ engagement with it, how this is managed by prison staff, what kind of teachers are involved in its delivery and why, if at all, inmates seem to lose interest in the education that is provided and indeed what should be done to motivate inmates and improve learning.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".