Global survey on evaluative reporting on DNA evidence with regard to activity‐level propositions
Bibliographic record
Abstract
For many criminal cases, the source of who deposited the DNA is not what the prosecutor and the defense are trying to dispute. In court, the question may be how the DNA was deposited at the crime scene rather than who the DNA came from. Although laboratories in many countries have begun to evaluate DNA evidence given formal activity-level propositions (ALPs), it is unknown how much other forensic practitioners know and what they think about activity-level evaluative reporting (ALR). To collect this information, a survey with 21 questions was submitted to international forensic science organizations across Europe, Australia, South America, Canada, Asia, and Africa. The survey combined open-ended and multiple-choice questions and received 162 responses. Responses revealed a wide range of knowledge on the topic. Overall, most respondents were somewhat knowledgeable about ALR, ALP, and current practices in court and expressed their support of the concept. A majority of participants identified gaps and obstacles regarding ALR they would like to see addressed. Examples include (1) need for more education/training at all stakeholder levels, (2) need for more DNA evidence-related data under realistic case scenarios, (3) need to internally implement and validate a formalized and objective approach for reporting, and (4) in some countries the need to achieve court admissibility. This global survey gathered the current concerns of forensic DNA practitioners and outlined several operational concerns. The information can be used to advance the implementation of ALR in laboratories and court testimony worldwide.
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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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".