Error rates for high confidence eyewitness identifications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Eyewitness identification of strangers is vulnerable to error, even if the eyewitness reports high confidence at the initial police identification procedure. In support of this claim, we report a new meta-analysis of data from actual criminal investigations. This analysis shows that when eyewitnesses were tested in the field by a blind lineup administrator, 1/8 of the high confidence identifications were known errors, i.e., mistaken identifications of lineup fillers. We argue that these field data are more informative than the available wrongful conviction data because in the latter eyewitness confidence at the initial identification procedure was almost never recorded. Our claim is also supported by lab data, which show that error rates for high-confidence identifications of the suspect can range from 0 to 40%, depending on the level of bias against the suspect. We highlight three types of suspect bias: appearance-based suspicion, social media contamination, and misplaced prior familiarity.
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.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it