The Structured Professional Judgement Approach to Violence Risk Assessment
Bibliographic record
Abstract
Abstract The Structured Professional Judgement (SPJ) approach is an analytical method used to understand and mitigate the risk for interpersonal violence posed by individual people that is discretionary in essence but relies on evidence‐based guidelines to systematize the exercise of discretion. The first SPJ guidelines were published in late 1994 and early 1995, making 2015 the 20th anniversary of the birth of the SPJ approach. This chapter takes the opportunity to retrospect and reflect on the history of the SPJ approach. It begins by reviewing the task of violence risk assessment, then three major approaches to violence risk assessment, including the SPJ approach, are compared and contrasted. Next, important milestones in the evolution of the SPJ approach, including recent advances, are outlined, and its current status compared with alternative approaches to risk assessment is evaluated. Finally, the future of the SPJ approach is discussed.
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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 | 0.069 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".