Four utilities in eyewitness identification practice: Dissociations between receiver operating characteristic (ROC) analysis and expected utility analysis.
Bibliographic record
Abstract
The present article focuses on a utility-based understanding of criminal justice practice regarding eyewitness identifications. We argue that there are 4 distinct types of utility that should be considered when evaluating an identification procedure. These include the utility associated with all identifications, the utility associated with only the high confidence identifications, the average utility across the full range of identifications, and the maximum utility that can be attained by selecting an ideal criterion. We show that in almost all cases in which the difference between 2 procedures is defined by a tradeoff between increased guilty suspect IDs and increased innocent suspect IDs, current ROC (receiver operating characteristic) curve approaches fail to provide unambiguous information about which eyewitness identification procedures are best in practice. We introduce a novel graphical technique called utility difference curves that illustrates the impact that differential assumptions about base rates and cost structures have on the likely benefits of different identification procedures. The research emphasizes the importance of considering assumptions about base rates and costs associated with different types of eyewitness errors. We also clarify situations in which the outcome of eyewitness experiments are unambiguous and those in which careful consideration of tradeoffs are necessary. (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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".