Postdictors of eyewitness errors: Can false identifications be diagnosed?
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 researchers have "postdicted" identification accuracy using witness confidence (S. L. Sporer, S. Penrod, D. Read, & B. Cutler, 1995), response latency (S. L. Sporer, 1993, 1994), and endorsement of statements consistent with using relative versus absolute judgment strategies (D. Dunning & L. B. Stern, 1994; R. C. L. Lindsay & K. Bellinger, 1999). All of these measures were collected from 321 introductory psychology students who had viewed a staged crime and completed a lineup identification task. Some participants received feedback after identification (G. L. Wells & A. L. Bradfield, 1998). Lineup fairness was also used as a postdictor of eyewitness accuracy. Discriminant function analysis indicated that 75.2% of choosers and 63.0% of nonchoosers were correctly classified. Decision time and lineup fairness were the best postdictors of accuracy. The implications for postdicting real eyewitness decisions 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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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 it