Bibliographic record
Abstract
Forensic psychiatry has been developing ethics guidelines over the last 50 years. The forensic psychiatry guidelines have taken a somewhat different path from traditional medical ethics based on beneficence and nonmaleficence. In particular, for forensic psychiatrists, the ethics concept of the primacy of striving for an individual's benefit may conflict with duties to the justice system. I posit that correctional psychiatry is a branch of forensic psychiatry that has ethics characteristics of both systems and discuss a way of resolving some of these dilemmas. Even if it is practiced by suitably qualified forensic psychiatrists, correctional psychiatry demands its own variation of ethics principles. This variation involves the additional variable of acknowledgment of the duty to the security of the institution. I develop this theory and apply it to some day-to-day ethics dilemmas with which correctional psychiatrists deal. Developing a code of ethics for correctional psychiatry is important. I apply a theoretical code of ethics to the many daily dilemmas experienced by correctional psychiatrists.
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 | 0.017 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.022 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".