Prosecution-retained versus court-appointed experts: Comparing and contrasting risk assessment reports in preventative detention hearings.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The goal of this study was to compare the risk assessment reports of prosecution-retained (n = 43) and court-appointed experts (n = 68) within the context of preventative detention hearings on variables ranging from the information within the assessment reports (e.g., length) to the conclusions drawn in terms of risk and treatment amenability (e.g., categorical statements of risk). A separate section also focused specifically on psychopathy. Court-appointed expert assessments were significantly longer (d = 0.40, 95% confidence interval [CI] [0.01, 0.78]) and contained more information pertaining to risk factors (odds ratio [OR] = 4.48, 95% CI [1.21, 16.61]) and risk management (OR = 3.15, 95% CI [1.20, 8.25]). Both types of experts communicated risk assessment results in categorical terms and were highly likely to utilize actuarial scales. Less than half of all assessments contained information on dynamic or protective factors. Other than providing a total psychopathy score, the assessments contained very little additional information about the implications of this score for risk management or treatment amenability. Although the results indicate that risk assessment reports between prosecution-retained and court-appointed experts were more similar than they were different, it is also evident that, overall, reports should contain more information on dynamic risk factors and risk management in order to be useful in the context of preventative detention hearings.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it