The impact of multiple show-ups on eyewitness decision-making and innocence risk.
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
If an eyewitness rejects a show-up, police may respond by finding a new suspect and conducting a second show-up with the same eyewitness. Police may continue finding suspects and conducting show-ups until the eyewitness makes an identification (Study 1). Relatively low criterion-setting eyewitnesses filter themselves out of the multiple show-ups procedure by choosing the first suspect with whom they are presented (Studies 2 and 3). Accordingly, response bias was more stringent on the second show-up when compared with the first, but became no more stringent with additional show-ups. Despite this stringent shift in response bias, innocence risk increased with additional show-ups, as false alarms cumulate (Studies 2 and 3). Although unbiased show-up instructions decreased innocent suspect identifications, the numbers were still discouraging (Study 4). Given the high number of innocent suspects who would be mistakenly identified through the use of multiple show-up procedures, using such identifications as evidence of guilt is questionable. Although evidence of guilt is limited to identifications from a single show-up, practical constraints might sometimes require police to use additional show-ups. Accordingly, we propose a stronger partition between evidentiary and investigative procedures.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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