Evaluation of the Forensic Science Regulator's recommendations regarding image comparison evidence
Bibliographic record
Abstract
Expert image comparison evidence can be a vastly helpful tool in the search for the truth, a central tenet of the criminal justice system. This evidence assists the court in determining the relationship between a questioned person, vehicle or object shown in video images with known facts and has been approved of judicially in several countries. The United Kingdom's Forensic Science Regulator has recently recommended significant restrictions on the use of such evidence, effectively relegating the video expert to a technical support role only and mandating the requirement for an image content expert. The author evaluates the recommendations and finds them to be overreaching. The Regulator is attempting to limit the use of a valid forensic science when in fact training and competence are the real issues. The author proposes a more restrained approach, one that does not usurp the role of the court in determining the admissibility of evidence.
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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 | 0.025 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.039 | 0.028 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".