Eyewitness Decision Processes: A Valid Reflector Variable
Bibliographic record
Abstract
ABSTRACT Identification accuracy can be predicted from eyewitnesses' self‐reported decision processes but the evidence of their ability to improve prediction when confidence and response time are included is mixed and minimal. Typically, decision processes are measured via one or five self‐report questions; we explored whether a more nuanced questionnaire could improve prediction. Participants viewed a mock‐crime video, made a target‐present or ‐absent lineup decision, and completed 17 decision process items. An exploratory factor analysis on choosers' ( n = 391) responses revealed three correlated factors, broadly reflecting automatic response, relative judgment, and absolute judgment. The three‐factor solution had good internal reliability (McDonald's ωs = 0.93, 0.89, and 0.74, respectively). Scores produced from the questions loading on the automatic response and relative judgment factors improved predictions of accuracy compared to using confidence and response time alone. Self‐reported decision processes may be an easy‐to‐administer and useful reflector of identification accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".