The State of Contemporary Risk Assessment Research
Bibliographic record
Abstract
The focus on assessing dangerousness in routine psychiatric practice developed when relatively little was known about factors related to violence, and the accuracy of predicting violence was distinctly below chance. Since the 1990s, however, significant research attention has been directed toward factors related to violence and mental illness, as well as toward factors related to the accuracy of risk assessment techniques. Sociodemographic and environmental variables have been identified as significant predictors of violence, as has the presence of substance abuse. However, the data on specific mental health variables are somewhat mixed. Many studies point to a modest increased risk of violence associated with major mental illness and psychosis, whereas other noteworthy studies have failed to confirm such findings. Studies of the accuracy of risk assessments indicate that both actuarial and clinical methodologies perform better than chance, although the former achieve greater statistical accuracy. Despite ongoing controversies, risk management strategies that encompass the strengths and limitations of our present knowledge are available to clinicians.
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 | 0.019 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".