New Insights on Expert Opinion About Eyewitness Memory Research
Bibliographic record
Abstract
Experimental psychologists investigating eyewitness memory have periodically gathered their thoughts on a variety of eyewitness memory phenomena. Courts and other stakeholders of eyewitness research rely on the expert opinions reflected in these surveys to make informed decisions. However, the last survey of this sort was published more than 20 years ago, and the science of eyewitness memory has developed since that time. Stakeholders need a current database of expert opinions to make informed decisions. In this article, we provide that update. We surveyed 76 scientists for their opinions on eyewitness memory phenomena. We compared these current expert opinions to expert opinions from the past several decades. We found that experts today share many of the same opinions as experts in the past and have more nuanced thoughts about two issues. Experts in the past endorsed the idea that confidence is weakly related to accuracy, but experts today acknowledge the potential diagnostic value of initial confidence collected from a properly administered lineup. In addition, experts in the past may have favored sequential over simultaneous lineup presentation, but experts today are divided on this issue. We believe this new survey will prove useful to the court and to other stakeholders of eyewitness research.
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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 | 0.106 | 0.205 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".