Mistaken eyewitness identification rates increase when either witnessing or testing conditions get worse.
Bibliographic record
Abstract
= 227), all participants watched the same clear culprit video but were then randomly assigned to either view a clear or noise-degraded lineup procedure. Half of the participants viewed a culprit-present lineup procedure and the remaining participants viewed a culprit-removed lineup procedure. Not surprisingly, degrading either encoding or retrieval conditions led to a sharp drop in culprit identifications. Critically, and as predicted, degrading either encoding or retrieval conditions also led to a sharp increase in the identification of innocent persons. These results suggest that when a lineup procedure gives a witness a weak match-to-memory experience, the witness will lower her criterion for making an affirmative identification decision. This pattern of results is troubling because it suggests that witnesses who encounter lineups that do not include the culprit might have a tendency to use a lower criterion for identification than do witnesses who encounter lineups that actually include the culprit. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".