On the Continuum of Foundational Validity: Lessons from Eyewitness Science for Latent Fingerprint Examination
Bibliographic record
Abstract
Whether forensic disciplines have established foundational validity-sufficient empirical evidence that a method reliably produces a predictable level of performance-has become a question of growing interest among scientists and legal professionals. This paper evaluates the foundational validity of two sources of forensic evidence relied upon in criminal cases: eyewitness identification decisions and latent fingerprint examiners' conclusions. Importantly, establishing foundational validity and estimating accuracy are conceptually and functionally different. Though eyewitnesses can often be mistaken, identification procedures recommended by researchers are grounded in decades of programmatic research that justifies the use of methods that improve the reliability of eyewitness decisions. In contrast, latent print research suggests that expert examiners can be very accurate, but foundational validity in this field is limited by an overreliance on a handful of black-box studies, the dismissal of smaller-scale, yet high-quality, research, and a tendency to treat foundational validity as a fixed destination rather than a continuum. Critically, the lack of a standardized method means that any estimates of examiner performance are not tied to any specific approach to latent print examination. Despite promising early work, until the field adopts and tests well-defined procedures, foundational validity in latent print examination will remain a goal still to be achieved.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".