Issues and considerations regarding the use of assessment instruments in the evaluation of competency to stand trial
Bibliographic record
Abstract
Since the early 1960s, a number of instruments, reflecting a broad range of assessment methods, have been developed to assist in the evaluation of competency to stand trial. These instruments have taken various forms including checklists, self-report questionnaires, sentence-completion tasks, and interview-based instruments with and without criterion-based scoring. This article reviews these assessment instruments with a specific focus on their contribution to the competency evaluation process. Furthermore, relevant issues and considerations regarding the use of these instruments are outlined, including a comparison of screening versus assessment applications of these instruments, balancing standardized approaches with individualized assessments, the integration of instrumentally derived data with other components of a competency evaluation, and the communication of results to the fact finder. Each of these issues is discussed in relation to specific competency assessment instruments. Overall, we argue that each of the competency assessment instruments developed to date can make a contribution to the competency evaluation process and this article serves to delineate those areas in which these contributions are made.
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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 | 0.626 | 0.728 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.001 | 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".