Cross-Cultural Validity of Actuarial Risk Assessment Instruments for Individuals in North America with a History of Sexual Offending: Static-99R and Static-2002R
Bibliographic record
Abstract
Cultural bias in structured risk assessment instruments is an important concern given the overrepresentation of certain racial/ethnic minority groups in the criminal justice systems of Canada and the U.S. The goal of this dissertation was to evaluate the cultural bias in predictive accuracy of widely used actuarial risk assessment instruments, Static-99R and Static-2002R, for the overrepresented ethnic minority groups in the criminal justice system in Canada (Indigenous peoples) and the U.S. (Blacks and Hispanics).Study 1 evaluated the predictive validity of Static-99R across three major ethnic groups (White, n = 789; Black, n = 466; Hispanic, n = 719) in the State of California.Static-99R was able to discriminate recidivists from non-recidivists among Whites, Blacks, and Hispanics with a history of sexual crimes (all area under the curve [AUC] values > .70;odds ratios > 1.39).Base rates (at a Static-99R score of 2) with a fixed 5-year follow-up across ethnic groups were very similar (2.4% -3.0%) but were significantly lower than the norms (5.6%).The current findings support the use of Static-99R in risk assessment procedures for individuals of White, Black, and Hispanic heritage; however, Static-99R should be used with caution in estimating absolute sexual recidivism rates, particularly for Hispanics with a history of sexual crime, as it may overestimate the absolute recidivism rates.Study 2 compares the characteristics and risk factors for non-Hispanic Whites (n =797) and Blacks (n = 788) who had been convicted of a sexual crime in New Jersey, USA.The results indicated that Whites appeared more paraphilic, whereas Blacks displayed higher anti-sociality.Despite the differences, the Static-99R predicted equally well for both racial groups: Whites (AUC = .76)and Blacks (AUC = .78).The findings suggest that there may be opportunities to improve treatment for Preface This dissertation is an integrated article thesis, the core of which are three papers by Seung C. Lee that have been accepted for publication or submitted for peer review.Given that these papers had co-authors, we assert that this dissertation is an original and independent work by Seung C. Lee, who was fully involved in setting up and conducting the research, obtaining data and analyzing results, as well as writing the materials presented in the dissertation.The three research papers are as follows: Study 1 (Lee & Hanson, 2017) was published in Criminal Justice and Behavior.The final, published version is reproduced in this dissertation with only minor edits
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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 | 0.030 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".