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 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 | 0.002 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".