“Only your first yes will count”: The impact of pre-lineup instructions on sequential lineup decisions
Bibliographic record
Abstract
When administering sequential lineups, researchers often inform their participants that only their first yes response will count. This instruction differs from the original sequential lineup protocol and from how sequential lineups are conducted in practice. Participants (N = 896) viewed a videotaped mock crime and viewed a simultaneous lineup, a sequential lineup with a first-yes-counts instruction, or a sequential control lineup (with no first-yes-counts instruction); the lineup was either target-present or target-absent. Participants in the first-yes-counts condition were less likely to identify the suspect and more likely to reject the lineup than participants in the simultaneous and sequential control conditions, suggesting a conservative criterion shift. The diagnostic value of suspect identifications, as measured by partial Area Under the Curve, was lower in the first-yes-counts lineup than in the simultaneous lineup. Results were qualitatively similar for other metrics of diagnosticity, though the differences were not statistically significant. Differences between the simultaneous and sequential control lineups were negligible on all outcomes. The first-yes-counts instruction undermines sequential lineup performance and produces an artefactual simultaneous lineup advantage. Researchers should adhere to sequential lineup protocols that maximize diagnosticity and that would feasibly be implemented in practice, allowing them to draw more generalizable conclusions from their data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.259 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".