Suspect filler similarity in eyewitness lineups: A literature review and a novel methodology.
Bibliographic record
Abstract
Eyewitness lineups typically contain a suspect (guilty or innocent) and fillers (known innocents). The degree to which fillers should resemble the suspect is a complex issue that has yet to be resolved. Previously, researchers have voiced concern that eyewitnesses would be unable to identify their target from a lineup containing highly similar fillers; however, our literature review suggests highly similar fillers have only rarely been shown to have this effect. To further examine the effect of highly similar fillers on lineup responses, we used morphing software to create fillers of moderately high and very high similarity to the suspect. When the culprit was in the lineup, a higher correct identification rate was observed in moderately high similarity lineups than in very high similarity lineups. When the culprit was absent, similarity did not yield a significant effect on innocent suspect misidentification rates. However, the correct rejection rate in the moderately high similarity lineup was 20% higher than in the very high similarity lineup. When choosing rates were controlled by calculating identification probabilities for only those who made a selection from the lineup, culprit identification rates as well as innocent suspect misidentification rates were significantly higher in the moderately high similarity lineup than in the very high similarity lineup. Thus, very high similarity fillers yielded costs and benefits. Although our research suggests that selecting the most similar fillers available may adversely affect correct identification rates, we recommend additional research using fillers obtained from police databases to corroborate our findings.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.032 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".