Bibliographic record
Abstract
The forensic psychiatric system is often criticized for being either too lenient or overly punitive, revealing deep-seated misconceptions about its operations and outcomes. This paper explores the systemic challenges faced by individuals with serious mental health conditions who intersect with the criminal justice system, focusing on the pervasive stigma, systemic biases and resource shortages that define their experiences. While the public frequently perceives forensic detention as lenient or preferential, the reality is starkly different, with individuals facing prolonged detentions and significant barriers to reintegration. Moreover, systemic issues such as racial discrimination, inadequate access to culturally appropriate care and a lack of supported housing exacerbate these challenges. Contrary to popular belief, the flaws in the system stem not from inadequacies in the law but from chronic under-resourcing of both forensic and civil mental health services. The paper concludes by advocating for improved inter-system collaboration, increased resource allocation and a shift in societal perceptions to address these entrenched issues effectively.
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 | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.022 |
| Scholarly communication | 0.017 | 0.035 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.016 | 0.027 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".