Do pre‐admonition suggestions moderate the effect of unbiased lineup instructions?
Bibliographic record
Abstract
We examined the additive and interactive effects of pre‐admonition suggestion and lineup instructions (biased or unbiased) on eyewitness identification rates. Participants watched a mock crime video, completed a target‐absent lineup identification, and completed a retrospective memory questionnaire. Prior to attempting an identification, participants were either exposed or not exposed to pre‐admonition suggestions and received biased or unbiased lineup instructions. The pre‐admonition suggestion indicated that it was likely that the perpetrator was in the lineup (surely, you can pick the perpetrator). The pre‐admonition suggestion increased false identification in the unbiased lineup condition. Furthermore, those who received the pre‐admonition suggestion were more certain in their identifications as well as other testimony‐relevant judgments than were those who did not receive the pre‐admonition suggestion. These results suggest that pre‐lineup suggestion can mitigate the beneficial effects of unbiased lineup instructions.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".