Bibliographic record
Abstract
The risk communication literature has shown mixed results about the most efficient way to communicate risk.In an attempt to standardize the way risk is communicated, Hanson, Bourgon and colleagues (2017) proposed the Five-Level Risk and Needs System.Despite the proposed benefits of the new system, the utility has yet to be thoroughly tested.The current study assessed whether utilizing the Five-Level System aids in the comprehension of risk and treatment amenability of a mock justice-involved individual.The study utilized a 3 x 2 design, manipulating risk level (low, moderate, high) and communication format (status quo, Five-Levels).Participants were asked to make decisions regarding parole, risk of recidivism, treatment amenability, among other risk, treatment, and understandability outcomes.Overall, limited support was found for communicating risk using the Five-Level System; however, there was evidence for improved consistency in risk perceptions, especially for participants presented with a moderate risk case.Findings suggested risk level was more salient than how risk was communicated.Keywords: Five-Level Risk and Needs System; risk assessment; risk communication; risk To my lab mates, Natasha 2 , I'm so glad we could complete this journey together, thank you for your support, advice, and guidance.To my friends and family, thank you for your love, kindness, support, for always being there for me, and participating in this study (I literally could not have done it without you).Finally, to my best friend and partner, Joshua, you are a more caring and selfless person than I will ever be
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 | 0.027 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".