Inner Speech Modification and Young Offender Re-offence: Literature Review and Implications
Bibliographic record
Abstract
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.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".