Can Decision-Making be Improved by Allowing Eyewitnesses to Opt-Out?: Examining the Utility of a ‘Not Sure’ Option With Showups
Bibliographic record
Abstract
I examined the impact of an explicit opt-out option on eyewitness identification performance. I predicted that an opt-out option would decrease innocent-suspect identifications more than culprit identifications, and that this effect would be more pronounced when viewing conditions were worse. I randomly assigned participants (N = 2003) to watch either a clear or degraded simulated-crime video. After a brief filler task, participants viewed either a culprit-present or culprit-absent showup and responded either "Yes" or "No". Half of the participants were randomly assigned to have an additional option to respond, "Not Sure". Contrary to my prediction, the not-sure option decreased both culprit (44% to 36%) and innocent-suspect (19% to 14%) identifications; this effect was unaffected by viewing condition quality. Despite empirical evidence and theoretical rationale indicating an opt-out option would improve the culprit and innocent-suspect identification tradeoff, the present results suggest otherwise.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".