Eyewitness Decision Processes: A Valid Reflector Variable
Bibliographic record
Abstract
Identification accuracy can be predicted from eyewitnesses’ self-reported decision processes but their ability to improve prediction beyond using confidence and response time alone has not been established. Typically, decision processes are measured via one or five self-report questions; we explored whether a more nuanced questionnaire could improve prediction. In 2015, participants from an Australian university viewed a mock-crime video (manipulated to create a strong or weak memory), viewed a target-present or -absent lineup, and completed 17 decision process items. An exploratory factor analysis on choosers’ (n = 391) responses revealed three correlated factors, broadly reflecting Automatic Responses, Relative Judgment, and Absolute Judgment. The three-factor solution had adequate fit for both memory strength conditions and good internal reliability y (McDonald’s ωs = .93, .89, and .74, respectively). Critically, scores produced from the questions loading on the Automatic Responses and Relative Judgment factors predicted unique variance in identification accuracy beyond what was accounted for by confidence and response time. Self-reported decision processes may be a practically useful reflector of identification accuracy.
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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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".