The Relevance of Trauma as a ‘Mental Disorder or Abnormality’ in Sentencing
Bibliographic record
Abstract
Trauma is the impact of adversity on psychological, emotional, neurophysiological, physical, and social functioning and well-being. Many defendants sentenced in mainstream courts have ‘mental disorders or abnormalities’ associated with trauma. Jurisdictions such as the United States, the United Kingdom, Canada, Aotearoa New Zealand, and Australia all divert a small number of defendants from prison into secure forensic mental health facilities if they are found not guilty by reason of mental impairment. However, many more defendants with the capacity to plead or be found guilty have been found to have neurophysiological and/or psychological trauma (approximately 50% to 90%). For these defendants, how might, and how should, their trauma-related ‘mental disorders or abnormalities’ influence sentencing? This chapter examines the relevance of psychological and neurophysiological trauma in sentencing, specifically reducing moral culpability and the relevance of deterrence, and informing effective sanctions. As the Victorian Court of Appeal case of R v. Verdins is a leading Australian authority on the relevance of mental impairments in sentencing, the six Verdins principles are applied to trauma suggesting how evidence-informed sentencing that focuses on trauma as an individualised ‘mental impairment’ can lead to better sentencing outcomes for defendants across jurisdictions.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".