Development of a competency test model to evaluate forensic identification officers on crime scene processing
Bibliographic record
Abstract
Proficiency testing is required to ensure quality, efficiency, and safety in many disciplines. Multiple proficiency tests exist for forensic disciplines such as fingerprint analysis and toxicology, but minimal research has been conducted on the proficiency of crime scene experts. The foundation of an effective proficiency test rests upon the development of competency tests. Here, a proof-of-concept competency test was designed as an example of how to evaluate the crime scene processing skills of forensic identification officers (FIOs) using a mock crime scene scenario. The test has three main components: i) crime scene approach test, ii) evidence processing test, and iii) general crime scene knowledge test, with pre-test demographic questions. The competency testing content and process was reviewed by two forensic identification experts (manuscript co-authors) for viability. Due to its digital format, this competency test is widely accessible, user-friendly, and can be a template for a police service to develop their own crime scene competency test or an internal proficiency test for specialized tasks such as a fingerprint comparison or an estimation of the area origin in bloodstain pattern analysis (BPA) to help mitigate risks and identify knowledge gaps.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".