Executive Cognitive Functioning Abilities of Male First Time and Return Canadian Federal Inmates
Bibliographic record
Abstract
A primary goal of forensic rehabilitation programming for incarcerated offenders is to eliminate or, at the very least, decrease rates of recidivism. However, repeat offending continues to occur, and studies suggest that reductions in recidivism brought about by programs are modest. Indeed, a series of studies suggests decreases in recidivism ranging between only 7% and 14%. While several factors have been identified as potential contributors to criminal behaviour, one notion that has garnered much attention is that an individual may be predisposed to criminality if s/he has deficits in executive cognitive functioning. At this time, the link between executive functioning and antisocial behaviour is largely unquestioned. However, it remains uncertain whether executive deficits may be even more profound in offenders who have served multiple terms of imprisonment. Using a cross sectional design, 93 Canadian federal inmates, categorized as either first timers (n=56) or return inmates (n=37) were tested on a battery of executive cognitive-functioning measures. In keeping with our hypotheses, return inmates showed more severe and pervasive patterns of executive dysfunction. These results suggest that improved focus on ameliorating ECF deficits of offenders may further assist in decreasing recidivism.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".