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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".