Sources of Variability in Estimates of Predictive Validity
Bibliographic record
Abstract
Level of Service (LS) is one of the most widely used general risk and need assessment tools in criminal justice agencies across North America. However, there is significant interstudy variability in the magnitude of the validity estimates. This study was conducted to examine possible sources of this variability. The predictive validity of LS risk and need increased with length of follow-up period and with investigator allegiance to LS. The combination of these two variables reveals consistent increases in mean predictive validity estimates from modest (in the .20s) through large (in the mid .30s) to very large (in the .40s) in samples of both male and female offenders. We hypothesized that the “allegiance effect” reflects the integrity of LS implementation and support provided by the agency for risk assessment. This is akin to the difference between “demonstration projects” and “practical” rehabilitation programming in the offender treatment research. Moreover, controls for Canadian versus non-Canadian evaluations reduced the effect of allegiance and length of follow-up to nonsignificant levels. Possible explanations for these findings include the degree of integrity in conducting risk and need assessments, the accuracy of recidivism as the criterion measure, and generalizability across international boundaries.
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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 | 0.185 | 0.534 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".