Eyewitness suspect identification: six claims regarding the state of the science
Bibliographic record
Abstract
Psychological science on eyewitness suspect identification has a long and rich history. A few decades ago, modal expert opinion emphasised eyewitnesses' fallibility, and it was widely held that identifications made with high confidence are only slightly more likely to be accurate than those made with low confidence. The authors of this invited Contemporary Discussion agree that current science compels a more nuanced perspective in which the relationship between eyewitnesses' confidence and their accuracy varies predictably depending on specifics of how the suspect was selected, how the identification test was designed, when and how it was administered, and when confidence was assessed. We tender claims regarding conditions under which we believe lineup identification responses can be strongly inculpating. We also articulate claims regarding conditions under which we believe identification responses can be strongly exculpating. While most of the claims described herein were previously advanced by individual scientists, what is new - and important - is that they now reflect an emerging scientific consensus. We do not assert that every claim is firmly established, but we advance arguments for believing they are true. In addition, we propose multiple lines of laboratory and field studies aimed at advancing understanding of these issues.
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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 | 0.097 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.058 |
| Scholarly communication | 0.015 | 0.029 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 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".