Making sense of confidence: do laypeople perceive eyewitness confidence in similar ways?
Bibliographic record
Abstract
Eyewitness researchers have recently concluded that there is a strong eyewitness confidence-accuracy relationship regardless of whether confidence is collected verbally or numerically; however, recent work indicates that the variability in how people use and interpret verbal confidence may limit the generalizability of this conclusion to practice. Furthermore, discussions of eyewitness confidence tend to refer to low-confidence or high-confidence eyewitnesses, but we have little knowledge about how people understand these categories. In two experiments, we explored how people understand low, medium, and high confidence, how they categorize numeric confidence statements, and how they interpret verbal confidence statements. Participants broadly agreed on what constituted low, medium, and high confidence, though interpretations of phrases, numbers, and categories were highly variable. Thus, legal officials and jurors may see the same confidence expression (whether phrase, number, or category) as indicating a different level of confidence than the eyewitness intended – an important source of error in the (confidence-accuracy) system: interpretations. Because (mis)interpretations can affect how eyewitness evidence is used, these findings underscore the need for a systematic approach to collecting and communicating eyewitness confidence that can facilitate common ground between eyewitnesses and those who must interpret their confidence.
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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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".