An Examination of How Various Statistical Weighting Methods Impact Predictive Validity of the Service Planning Instrument for Women (SPIn-W)
Bibliographic record
Abstract
Risk assessments are vital within the criminal justice system, yet research regarding the optimization of these instruments for women is limited.Currently, minimal research is available on the impact various statistical weighting methodologies may have on the prediction of recidivism for women.Using two-year fixed follow-up data from 656 justice-involved women from Maine United States, the current study explored the predictive validity of the Service Planning Instrument for Women (SPIn-W; Orbis Partners, 2007) at the item level and the predictive accuracy of four weighting methodologies.Results from the present study showed that 19 of the 98 items of the SPIn-W were significantly predictive of recidivism.Further, the genderresponsive Nuffield 2.0 weighting method most often evidenced the greatest levels of predictive accuracy across aggregate and domain level scores.Pending replication and cross-validation, the current study suggests that the SPIn-W be updated with the gender-responsive Nuffield 2.0 method to optimize predictive validity.
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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 | 0.140 | 0.284 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".