Forensic epistemology: A need for research and pedagogy
Bibliographic record
Abstract
This is the third in a series of articles reporting on forensic epistemology. Our first two research articles presented scientific results that are based in experimental design; including quantitative and qualitative responses from forensic science practitioners to scenarios and evidence. Based on a synthesis of this research there is evidence of a knowledge gap in formal reasoning for some forensic practitioners, and a limited understanding of case-specific research. Combining these results with a review of the current literature in the field of forensic reasoning, we now offer evidence of teaching and research strategies that can help increase the epistemic status (Confidence in, and justification of knowledge) of forensic science claims. This paper focuses on an integrated narrative review using hermeneutic methods of analysis to identify: (i) the epistemic state of forensic science; (ii) strategies to increase of knowledge; (iii) the need for collaboration between practitioners and academics; and, (iv) areas for future research.
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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 | 0.087 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.010 | 0.049 |
| Scholarly communication | 0.032 | 0.063 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 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".