Towards another paradigm for forensic science?
Bibliographic record
Abstract
Abstract Daubert skews the contribution of forensic science because it only took into account its Galilean dimension (construction of general predictive models). However, forensic science should better be classified in the historical sciences (clinical approach to reconstruct a past event of presence or activity). We therefore need a complementary approach that integrates the necessarily “clinical” part in the resolution of forensic issues. Such an evolution involves semiotics. While recognizing that the Bayesian way of thinking is the only prescriptive available model for interpretation fitting well in the Galilean paradigm, the complexity of the reconstruction of a past‐uncontrolled singular case and the robustness of available relevant data to it, invites consideration of its implementation in a semiotic line of arguments. Indeed, Bayes makes it possible to remain in a single harmonized model integrating both the clinical and Galilean dimensions, but rapidly the complexity of the modeling and its mathematization come up against more qualitative natural and legal reasoning. Two different systems of reasoning at stake are inevitably creating a “bug” that could explain the current forensic crisis and miscarriages of justice. This anomaly is reflected in the issue of transparency (misunderstandings by and between interlocutors on the nature of the expertise, if not science). Peirce offers a path to address the tension between complementary reasoning systems. This article is categorized under: Crime Scene Investigation > Epistemology and Method Crime Scene Investigation > From Traces to Intelligence and Evidence Crime Scene Investigation > Education and Formation
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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 | 0.030 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.007 | 0.094 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.013 |
| 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".