Towards cumulative forensic science: a commentary on ‘Methodological problems in every black-box study of forensic firearm comparisons’
Bibliographic record
Abstract
Abstract Cuellar et al. recently found that methodological flaws in black-box studies of forensic firearms analysis mean that validity cannot be determined from those studies. Their paper can also be read to indicate that the presence of some of these flaws means that the associated study is so unsound that it can only be used to plan future properly designed validation studies. We seek to clarify that each of the identified flaws, taken individually, does not necessarily prevent studies from contributing to a strong, cumulative research basis for forensic practices. That said, we agree that the overall body of research must avoid the flaws identified by Cuellar et al., and, based on their analysis, it appears the overall body of research has not avoided these flaws. We go on to suggest practices that can help ensure forensic science studies can efficiently and safely build on each other.
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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 | 0.164 | 0.446 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.014 | 0.073 |
| Scholarly communication | 0.020 | 0.038 |
| Open science | 0.019 | 0.011 |
| Research integrity | 0.104 | 0.160 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".