Lineup administrators' expectations: Their impact on eyewitness confidence.
Bibliographic record
Abstract
This research focuses on how lineup administrators influence eyewitnesses' postidentification confidence. What happens to witness confidence when a witness makes an identification that confirms the lineup administrator's expectations; what happens when this expectation is not confirmed? In Experiment 1, participant interviewers (n = 52) administered target-absent photo lineups to participant witnesses (n = 52). The interviewers did not view the simulated crime, but were told the thief's position in the lineup. In every instance this information was false (we used a target-absent lineup). A one-way ANOVA revealed that eyewitness identification confidence was malleable as a function of interviewers' beliefs about the thief's identity. In Experiment 2, participant jurors (n = 80) viewed 40 testimonies of Experiment 1 witnesses (2 participants viewed each testimony). Participant jurors judged all participant witnesses as equally credible despite their varying levels of postidentification confidence.
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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 | 0.004 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".