A Bayesian analysis on the (dis)utility of iterative-showup procedures: The moderating impact of prior probabilities.
Bibliographic record
Abstract
A showup is an identification procedure in which a lone suspect is presented to the eyewitness for an identification attempt. Showups are commonly used when law enforcement personnel locate a suspect near the scene of a crime in both time and space but lack probable cause to make an arrest. If an eyewitness rejects a suspect from a showup, law enforcement personnel might find another suspect and run another showup. Indeed, law enforcement personnel might go through several iterations of finding suspects and running showups with the same eyewitness. We label this phenomenon the iterative-showup procedure. The consequence of this procedure is that innocent suspect identifications increase disproportionately to culprit identifications. This happens because there is only one culprit, but a seemingly endless supply of innocent suspects. We apply Bayesian modeling to single- and iterative-showup procedures to demonstrate that iterative showups are almost always associated with lower probative value. We demonstrate that the prior probabilities that later suspects are the culprit are greatly constrained by the posterior probabilities that earlier suspects were the culprit. Identifications from iterative-showup procedures are of questionable reliability. We review alternative investigative strategies that police might consider in order to limit the use of iterative-showup procedures. (PsycINFO Database Record
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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 | 0.092 | 0.485 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".