Examiner consistency in perceptions of fingerprint minutia rarity
Why this work is in the frame
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Bibliographic record
Abstract
Friction ridge examiners (FREs) identify distinctive features (minutiae) in fingerprints and consider how rare these observed minutiae are in their decisions about both the value of a fingerprint and whether there is enough correspondence between two fingerprints to support an “identification” or “exclusion” decision. But subjective perceptions about the frequency of events and features tend to be inconsistent and dynamic, which means that variable perceptions of minutia frequency may contribute to inconsistencies in FREs’ opinions about fingerprint evidence. We surveyed expert FREs at two time points ( N Time 1 = 132; N Time 2 = 99) to establish how rare FREs believe different minutia types to be and to determine the variation in examiners’ perceptions—both between different examiners and across time for the same examiner. We observed significantly less variation in FREs’ perceptions of minutia frequency for three minutiae: the two most common minutiae and the minutia perceived to be the least common. We also observed increases in FREs’ estimates of minutia frequency over time and when they reported recent sightings of the rarest minutiae. FREs reported frequently using this information in their fingerprint comparison decisions. We present practical recommendations for using these consensus-based frequency estimates (until more objective data are available) to increase consistency in FREs’ use of base rates when examining fingerprint evidence, which may consequently increase the repeatability and reproducibility of decisions made by FREs. • Most LPEs consider minutia rarity when examining friction ridge impressions. • LPEs’ estimates of minutia rarity vary, especially if perceived as moderately common. • LPEs’ estimates of minutia rarity vary over time and based on recent experience. • LPE survey results identify consensus-based estimates of minutia base rates. • Use of consensus-based estimates may improve the reliability of LPEs’ decisions.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it