Bibliographic record
Abstract
F. S. Berlin, N. W. Galbreath, B. Geary, and G. McGlone (this issue) have raised some important questions regarding the use of acturial risk assessment instruments in sex offender civil commitment proceedings, also known as sexually violent predator or SVP proceedings. Their primary point is that interpreting the findings of existing actuarial risk assessment instruments is a tricky business because it is not certain whether the extent to which probability estimates derived from group data can be applied to individual cases. I agree completely with Berlin et al. on this point, but disagree with them concerning the extent to which probability estimates--and, therefore, actuarial instruments--are legally relevant in SVP proceedings. I outline some potential problems with respect to the legal admissibility of actuarial instruments, including their legal relevance.
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 | 0.041 | 0.221 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.013 | 0.004 |
| Research integrity | 0.086 | 0.093 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".