Using Graphs in Sexual Violence Risk Communication: Benefits May Depend on the Risk Metric
Bibliographic record
Abstract
Actuarial scales provide a relatively objective and reliable assessment of individuals’ risk of recidivism. Recent research has explored how graphs can improve quantitative risk communication. We tested whether graphs can improve understanding and perception of sexual violence risk when matched with risk metric. Participants ( N = 676) were recruited from Amazon’s MTurk platform and read a brief description of a man convicted of a sexual offense, including results of a fictional sexual recidivism risk scale. In Study 1, absolute risk of recidivism enabled participants to distinguish between individuals with relatively high and low risk of sexual recidivism. In Study 2, this distinction was enhanced by adding a graph, especially when percentiles were communicated. Risk ratios increased perceived risk. Objective numeracy increased understanding and reduced perceived risk. We recommend that risk communication assumes limited statistical numeracy, and further research with practitioners to test the effect of graphs and risk metrics on forensic/judicial decisions.
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How this classification was reachedexpand
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".