The interpretation of forensic conclusions by criminal justice professionals: The same evidence interpreted differently
Bibliographic record
Abstract
Forensic reports use various types of conclusions, such as a categorical (CAT) conclusion or a likelihood ratio (LR). In order to correctly assess the evidence, users of forensic reports need to understand the conclusion and its evidential strength. The aim of this paper is to study the interpretation of the evidential strength of forensic conclusions by criminal justice professionals. In an online questionnaire 269 professionals assessed 768 reports on fingerprint examination and answered questions that measured self-proclaimed and actual understanding of the reports and conclusions. The reports entailed CAT, verbal LR and numerical LR conclusions with low or high evidential strength and were assessed by crime scene investigators, police detectives, public prosecutors, criminal lawyers, and judges. The results show that about a quarter of all questions measuring actual understanding of the reports were answered incorrectly. The CAT conclusion was best understood for the weak conclusions, the three strong conclusions were all assessed similarly. The weak CAT conclusion correctly emphasizes the uncertainty of any conclusion type used. However, most participants underestimated the strength of this weak CAT conclusion compared to the other weak conclusion types. Looking at the self-proclaimed understanding of all professionals, they in general overestimated their actual understanding of all conclusion types.
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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 | 0.029 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".