The impact of multiple show-ups on eyewitness decision-making and innocence risk.
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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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 | 0.035 | 0.241 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".