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.
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 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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".