How to measure lineup fairness: concurrent and predictive validity of lineup-fairness measures
Bibliographic record
Abstract
The current study examined the concurrent and predictive validity of four families of lineup-fairness measures – mock-witness measures, perceptual ratings, face-similarity algorithms, and resultant assessments (assessments based on eyewitness participants’ responses) – with 40 mock crime/lineup sets. A correlation analysis demonstrated weak or non-significant correlations between the mock-witness measures and the algorithms, but the perceptual ratings correlated significantly with both the mock-witness measures and the algorithms. These findings may reflect different task characteristics – pairwise similarity ratings of two faces versus overall similarity ratings for multiple faces – and suggest how to use algorithms in future eyewitness research. The resultant assessments did not correlate with the other families, but a multilevel analysis showed that only the resultant assessments – which are based on actual eyewitness choices – predicted eyewitness performance reliably. Lineup fairness, as measured using actual eyewitnesses, differs from lineup fairness as measured using the three other approaches.
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 | 0.026 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".