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Record W2886020357

Does prison education in Scottish young offenders’ institutions work?

2016· article· en· W2886020357 on OpenAlexaboutno aff
Yonah Hisbon Matemba

Bibliographic record

VenueThe UWS Academic Portal (University of the West of Scotland) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPrisonCriminologyWork (physics)Political sciencePsychologySociologyEngineering
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.586
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.273
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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