Factors Affecting Sexual Assault Case Processing: Charging Through Sentencing in a Large Southern County
Bibliographic record
Abstract
Factors affecting prosecutorial decision-making represent one of the most understudied parts of the criminal justice system. Documenting these influences in relation to sexual assault cases is even more rare. The present study analyzed the complete prosecutorial case files of a large, southern district attorney’s office regarding all adult sexual assault cases received over a three-year period. Logistic regression and continuation ratio modeling were used to determine which factors were related to continued progression through the court system, from charging to sentencing. The findings indicate that cases with older or Latino defendants, as well as cases involving injury to the victim, were significantly more likely to be charged. A continuation ratio model of subsequent case outcomes indicated that factors such as DNA evidence, the use of a weapon, and the inclusion of a victim impact statement increased the likelihood of a case progressing to later stages of the system. The influence of criminal history and the amount of prosecutor contact with the victim, however, varied across outcomes. Namely, criminal history increased the odds of receiving a prison sentence while prosecutor contacts with the victim increased the odds of case indictment. These findings imply potential shifts in the treatment of these cases while also suggesting areas of improvement. Namely, prosecutors should strive to increase the amount of meaningful contacts with victims and encourage their participation in the court process. These findings also support the use of sexual assault packets by law enforcement to improve and standardize reporting practices for these cases.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".