Forensic epistemology: exploring case-specific research in forensic science
Bibliographic record
Abstract
Our inquiry into forensic epistemology explores the use of data types for case-specific research within three pattern interpretation disciplines. It also examines the epistemic status of practitioner case experimentation in forensic science. We developed three cases from different pattern-interpretation disciplines: a friction ridge analysis; a bloodstain pattern analysis; and a footwear impression analysis. For each case, a series of experiments were derived using three different data types: a quantitative approach (using numeric data), a qualitative approach (using image data) and a mixed-method approach (using both numeric and image data). We supplied data analyses that would be common knowledge for any academic researcher. Electronic files were compiled for each case and research method and forwarded by Qualtrics Software to forensic practitioners within the prescribed discipline. Demographic questions on practitioner education level and years of experience were included in the survey, along with open-ended comment areas. The dependent variable is the participants’ percentage confidence in providing an opinion from the data type used. ANOVA analyses indicated that the practitioners were more confident using a mixed-method data approach. No differences were found between the percentage confidence levels and discipline type. Similarly, there was no significant difference between the confidence levels and years of experience or the participants’ education level. The qualitative data analysis validated the quantitative results in that the practitioners were more confident with a mixed-method research approach.
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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 | 0.138 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.015 | 0.056 |
| Scholarly communication | 0.023 | 0.033 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".