The elimination‐plus lineup: Testing a modified lineup procedure with confidence
Bibliographic record
Abstract
Abstract The present study examined whether a modified form of a preidentification confidence rating would provide evidence of a suspect's guilt in addition to the identification decision confidence. Participants ( N = 241) viewed a videotaped mock crime and were presented with a target‐present or target‐absent simultaneous, sequential, elimination, or elimination‐plus lineup procedure; both elimination procedures required 2 separate judgments from the witness (i.e., relative and absolute). The elimination‐plus procedure was identical to that of the elimination procedure with the addition of the confidence rating in between judgment 1 and judgment 2. Confidence after judgment 1, confidence after judgment 2, and the average of the 2 confidence ratings with the elimination‐plus procedure significantly predicted accuracy for choosers. Given that confidence has been recognised by the Supreme Court of the United States, these results shed light on a novel way of utilising confidence in the investigative process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".