Wrongful Conviction in Texas: ‘Sex Assaults’, False Guilty Pleas, Stranger Rape with Misidentification, and Drug Offenses
Bibliographic record
Abstract
Abstract Citing IP data, Johnson (2021), reported that sexual assault/rape was the most common offense associated with exoneration in the US. Also, stranger rape accounted for 72% of the entire IP database. To further examine the role of sexual assault, the current study examined all exonerations in Texas, the US state with the most sexual assault exonerations. Using NRE data, descriptive analyses, and reclassifying sexual assaults, we find drug offenses are the most common crime type associated with exonerations in Texas but sexual assault/rape accounts for a significant portion of Texas exonerations. Contrary to a common assumption, we also find that exculpatory DNA does not explain the substantial proportion of sexual assaults among exonerations. We also examine the role of stranger rape misidentification, youthful complainant recantations (perjury/false allegations) and false guilty pleas in the NRE Texas database. Finally, we discuss other patterns within the Texas exonerations and policy implications. Keywords: Rape, Exoneration, Misidentification, Pleas
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".