Fingerprint Comparison and Adversarialism: The Scientific and Historical Evidence
Bibliographic record
Abstract
Abstract This article suggests that lawyers and courts are largely oblivious to scientific insights regarding the value and limitations of latent fingerprint evidence. It proceeds through a detailed historical analysis of the way fingerprint evidence has been reported and challenged. It compares legal responses with mainstream scientific research. Our analysis shows that fingerprint evidence is routinely equated with categorical proof of identity notwithstanding scientific warnings that such an approach is ‘indefensible’. We find that legal challenges to latent fingerprint evidence have been uniformly focused on adjectival issues (e.g. compliance with enabling legislation), leaving the validity and accuracy of this subjective comparison technique virtually unexamined since its first reception at the very beginning of the twentieth century. Lack of legal engagement with validity, error and scientific research suggest that adversarial procedures have not worked effectively to secure scientifically reliable expert evidence and that legal personnel struggle with elementary scientific reasoning.
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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 | 0.047 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.038 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".