Witnessing-condition heterogeneity and witnesses' versus investigators' confidence in the accuracy of witnesses' identification decisions.
Bibliographic record
Abstract
Undergraduate participants were tested in 144 pairs, with one member of each pair randomly assigned to a "witness" role and the other to an "investigator" role. Each witness viewed a target person on video under good or poor witnessing conditions and was then interviewed by an investigator, who administered a photo line up and rated his or her confidence in the witness. Witnesses also (separately) rated their own confidence. Investigators discriminated between accurate and inaccurate witnesses, but did so less well than witnesses' own confidence ratings and were biased toward accepting witnesses' decisions. Moreover, investigators' confidence made no unique contribution to the prediction of witnesses' accuracy. Witnesses' confidence and accuracy were affected in the same direction by witnessing conditions, and there was a substantial confidence-accuracy correlation when data were collapsed across witnessing conditions. Confidence can be strongly indicative of accuracy when witnessing conditions vary widely, and witnesses' confidence may be a better indicator than investigators'.
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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 | 0.015 | 0.183 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".