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Record W3106422315 · doi:10.29173/bsuj367

Inner Speech Modification and Young Offender Re-offence: Literature Review and Implications

2019· article· en· W3106422315 on OpenAlexaffvenue
Jessica Elsom

Bibliographic record

VenueBehavioural Sciences Undergraduate Journal · 2019
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsMount Royal University
FundersUniversidad de GranadaUniversity of Oxford
KeywordsRecidivismPsychologyFlexibility (engineering)Intervention (counseling)Variety (cybernetics)RehabilitationDevelopmental psychologyApplied psychologyClinical psychologyComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

Inner speech is the voice in our heads that serves a variety of functions, and impacts individuals’ thoughts and behaviours. It is thought that young offenders have misguiding inner voices, and there is hope that professionals can change this through inner speech modification. During treatment, practitioners attempt to teach young offenders to use skills and tools. Ideally, this will reduce recidivism rates and allow these youth to become contributing members of society. In this literature review, the relationship between inner speech and young offender reoffence is examined. The purpose of this research is to bridge literature on inner speech, cognitive behavioural therapy, and young offender research to provide a source of suggestions for reducing delinquent behaviours. I advocate for inner speech modification programs in young offender rehabilitation because the research presented in this review supports the use of innerspeech in behaviour modification. I argue that the programs designed for young offenders need continued flexibility, and that there needs to be an increase in program availability for young offenders, especially ones involving inner speech modification. I also suggest that researchers should examine more preventative, earlier intervention programs, and investigate the relationships between inner speech and language deficiencies in young offenders.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.053
GPT teacher head0.334
Teacher spread0.281 · 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

Citations1
Published2019
Admission routes2
Has abstractyes

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