Bibliographic record
Abstract
PURPOSE OF REVIEW: Competence to be sentenced, or lack thereof, is not frequently claimed in legal proceedings. This paper reviews the concept and possible forensic psychiatric applications. RECENT FINDINGS: This topic has achieved prominence because of recent rulings imposed by the US Supreme Court that prohibit execution of mentally impaired and under-age individuals. The most recent contributions focus on standards for evaluating competence to be sentenced and on analyzing the need for involuntary treatment of those who for psychiatric reasons have lost the capacity to stand trial. SUMMARY: Competence to be sentenced may be defined as a component of a general capacity to undergo legal proceedings, beyond just fitness to stand trial. It applies specifically to the time between the moment the process ends and the moment a sentence is rendered. This paper reviews general capacity to participate in legal proceedings as a concept that allows a person to intervene fully in his or her defence in a trial that is just and fair, and the different moments at which mental capacity to proceed may be limited or absent. There are no specific guidelines for evaluating competence to be sentenced, so instead we review the basic criteria for general capacity to participate in legal proceedings, including the capacity to mount a defence, to be sentenced and to serve a sentence. Because forensic evaluations are needed for assessment of capacity, the paper provides guidelines on how to organize the assessment and the report.
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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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".