Forensic epistemology: testing the reasoning skills of crime scene experts
Bibliographic record
Abstract
In recent years, crime scene analysis has been transitioning from being a technical discipline to being a scientific process. This progression is shifting the forensic practitioner examining crime scenes into a deeper level of scientific reasoning. This study evaluates the use of reasoning by practitioners in the disciplines of crime scene investigations and bloodstain pattern analysis. A well-established classroom test of scientific reasoning (CTSR) was distributed online to active crime scene investigators (CSI) and bloodstain pattern analysts (BPA) (n = 213) using Qualtrics software. The survey provides quantitative data on the reasoning ability of the participating practitioners along with demographic information on education, employment status (specifically, police or civilian), and work experience. Linear regression analyses indicate that there is a significant difference between the CTSR scores and education level. The higher educated practitioner (graduate level) performed better on the reasoning test. No significant differences were found between the test scores and the years of experience, even when sectioned into 5-year increments of 5 to 25+ years of experience. Similarly, there was no difference between the test scores and employment status for the CSI group and within the BPA group. This information suggests that the level of education plays the most important role in the development and use of reasoning skills, whereas experience and employment status are not as influential. The test scores were also mapped to Piaget’s categories – concrete operational, transitional and formal operational reasoners – with 69.5% of CSI and 77% of BPA scoring as formal operational reasoners. The authors recommend that a CTSR be used for testing current and future (tertiary forensic students) practitioners for evaluating reasoning skills and identifying scientific learning gaps. This study also supports further research into forensic epistemology and pedagogy, to deepen our knowledge of science in forensic science.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 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".