Eyewitness Lineup Identity (ELI) database: Crime videos and mugshots for eyewitness identification research
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
There is a long history of experimental research on eyewitness identification, and this typically involves staging a crime for participants to witness and then testing their memory of the "culprit" by administering a lineup of mugshots. We created an Eyewitness Lineup Identity (ELI) database, which includes crime videos and mugshot images of 231 identities. We arranged the mugshots into 6-, 9-, and 12-member lineups, and then we tested the stimuli in an eyewitness experiment. Participants (N = 1584) completed six trials of viewing a crime video and completing a lineup identification task. In lineups that included the culprit, the average probability of correction identification was 59.0%, 95% CI [55.9, 62.0]. In lineups that did not include the culprit, the average probability of false alarm was 29.9% [27.8, 32.0]. These outcomes indicate that the ELI database is suitable for eyewitness identification research, and the large number of crime videos would enable stimulus sampling. The database is available for research approved by a research ethics board and can be requested at https://osf.io/vrj3u .
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 | 0.030 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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