Bibliographic record
Abstract
Flawed eyewitness testimony, faulty forensics, and police misconduct are common factors that may contribute to wrongful conviction. However, what brings someone over the threshold of suspicion where these factors are used to build the case against them? To answer that question, we built upon the limited number of previous studies examining how someone becomes a suspect in serious crimes (e.g., murder, rape). This exploratory study utilized Innocence Project materials pertaining to 232 exonerated clients and 75 individuals for whom post-conviction DNA testing was found to be an “inclusion” (i.e., supportive of the prosecution’s theory of guilt). Based on case files, we coded pathways to becoming a suspect. These pathways included tips, matched description, previous law enforcement encounters, physical evidence, and other scenarios; more than one pathway could be used for each individual. While several pathways were found to be similar in both groups, differences were seen in pathways related to physical evidence, officers putting individuals under duress during questioning, and proximity to the crime. This exploratory analysis provides a basis for designing future hypothesis-based research to further examine the observed associations and provide further insights into the investigative processes that can lead to wrongful convictions.
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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| 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".