The role of employment/training and its relationship to crime-free living through the voices of former Australian inmates
Bibliographic record
Abstract
This study explores employment/training experiences of adult Australian ex-inmates living crime-free. Little is known in terms of how employment/training comes to assist ex-inmates in living crime-free and, equally, what role employment/training has played in the lives of ex-inmates prior to and during incarceration. Integrating both qualitative and quantitative methods, employment/training was measured at pre-incarceration, during incarceration and post-incarceration to explore its relationship with crime-free living. All participants (n = 20) reported having employment prior to incarceration, but only a quarter reported that it was stable and secure. Participants also reported having ad hoc jobs during incarceration with less than one-fifth having post-prison employment. However, for a small group of participants who persisted with employment/training, they reported increased self-esteem. They also reported that employment/training was an informal social control alongside inherent social incentives of being a productive citizen. Therefore, employment may perhaps be a notable catalyst for successful transition/reintegration for those ex-inmates who actively seek out and persist with it. Importantly, over half of the participants reported that employment was not related to recidivism and/or safeguarding them from re-offending or in living crime-free.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".