Trends and challenges in forensic research on offenders with intellectual disability
Bibliographic record
Abstract
BACKGROUND: The Journal of Intellectual & Developmental Disability has a well-respected history of establishing the parameters and contributing to developments in the field of offenders with intellectual disability (ID). METHOD: The field has seen a number of developments over the past 15 years, and this paper identifies several trends that have emerged in the research during this period, including work on prevalence of ID in prison populations, development of risk assessment, consideration of staff issues, developing the psychometrics of offence-specific assessments, evaluating treatment methods, and testing the underlying theoretical frameworks which attempt to account for offending. RESULTS AND CONCLUSIONS: We refer to a number of studies which have advanced these developments in the field and draw the reader's attention to the way in which papers in this special issue contribute to and further develop each of these research trends.
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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 | 0.034 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.023 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".