How variations in distance affect eyewitness reports and identification accuracy.
Bibliographic record
Abstract
Witnesses observe crimes at various distances and the courts have to interpret their testimony given the likely quality of witnesses' views of events. We examined how accurately witnesses judged the distance between themselves and a target person, and how distance affected description accuracy, choosing behavior, and identification test accuracy. Over 1,300 participants were approached during normal daily activities, and asked to observe a target person at one of a number of possible distances. Under a Perception, Immediate Memory, or Delayed Memory condition, witnesses provided a brief description of the target, estimated the distance to the target, and then examined a 6-person target-present or target-absent lineup to see if they could identify the target. Errors in distance judgments were often substantial. Description accuracy was mediocre and did not vary systematically with distance. Identification choosing rates were not affected by distance, but decision accuracy declined with distance. Contrary to previous research, a 15-m viewing distance was not critical for discriminating accurate from inaccurate decisions.
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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 | 0.002 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".