Of Earprints, Fingerprints, Scent Dogs, Cot Deaths and Cognitive Contamination: A Brief Look at the Present State of Play in the Forensic Arena
Bibliographic record
Abstract
Over the last decades, the importance of technical and scientific evidence for the criminal justice system has been steadily increasing. Unfortunately, the weight of forensic evidence is not always easy for the trier of fact to assess, as appears from a brief discussion of some recent cases in which the weight of expert evidence was either grossly over- or understated. Also, in recent years, questions surrounding the value of forensic evidence have played a major role in the appeal and revision stages of a number of highly publicized criminal cases in several countries, including the UK and the Netherlands. Some of the present confusion is caused by the different ways in which conclusions are formulated by experts working within the traditional approach to forensic identification, as exemplified by (1) dactyloscopy and (2) the other traditional forensic identification disciplines like handwriting analysis, firearms analysis and fibre analysis, as opposed to those working within the modern scientific approach used in forensic DNA analysis. Though most clearly expressed in the way conclusions are formulated within the diverse fields, these differences essentially reflect the scientific paradigms underlying the various identification disciplines. The types of conclusions typically formulated by practitioners of the traditional identification disciplines are seen to be directly related to the two major principles underpinning traditional identification science, i.e. the uniqueness assumption and the individualization principle. The latter of these is shown to be particularly problematic, especially when carried to its extreme, as embodied in the positivity doctrine, which is almost universally embraced by the dactyloscopy profession and allows categorical identification only. Apart from issues arising out of the interpretation of otherwise valid expert evidence there is growing concern over the validity and reliability of the expert evidence submitted to courts. While in various countries including the USA, Canada and the Netherlands criteria have been introduced which may be used as a form of input or output control on expert evidence, in England and Wales expert evidence is much less likely to be subject to forms of admissibility or reliability testing. Finally, a number of measures are proposed which may go some way to address some of the present concerns over the evaluation of technical and scientific evidence.
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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 | 0.000 |
| 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".