Towards another paradigm for forensic science?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it