Identifying Patterns Across the Six Canonical Factors Underlying Wrongful Convictions
Bibliographic record
Abstract
Research has established six “canonical” factors underlying wrongful convictions including: mistaken witness identification (MWID), false confession (FC), perjury or false accusation (P/FA), false or misleading forensic evidence (F/MFE), official misconduct (OM), and inadequate legal defense (ILD). While we know these factors do not occur in isolation, researchers have yet to examine the patterns across these six factors. In the present article, we apply latent class analysis to explore how these six factors might co-occur across known exonerations. Using data from the National Registry of Exonerations, we identify four latent classes by which the incidence rates across these six factors can be categorized. Among our noteworthy findings: 1) P/FA and OM often co-occur, 2) when MWIDs are high, the incidence of other factors is relatively low, and 3) false guilty pleas had the highest prevalence in a class that was generally associated with Failures to Investigate. Further implications are discussed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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; both teacher heads agree on what is shown here.
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".