Variability in verbal eyewitness confidence
Bibliographic record
Abstract
Abstract Typically, an eyewitness' verbal confidence is used to judge the reliability of their lineup identification. Across three experiments ( N = 3976), we examined eyewitnesses' own words confidence in their lineup decision. For identification decisions ( n = 1099), we identified 781 quantitatively unique responses representing 132 qualitatively unique statements that could be categorized into low, medium, and high confidence. For rejectors ( n = 781), we identified 599 quantitatively unique responses representing 143 qualitatively unique responses that could be categorized into low, medium, and high confidence. Most participants provided a verbal phrase (e.g., pretty sure) but a significant proportion—34.19% of identifiers and 29.05% of rejectors—provided numbers (e.g., 80%). The present data highlight the variability in how confidence is expressed. The criminal justice system would benefit from guidance for interpreting verbal confidence. We provide a picture of eyewitnesses' verbal confidence as a first step.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".