Trial by Theory: A Response to Acharya's "Law's Treatment of Science: From Idealization to Understanding"
Bibliographic record
Abstract
Adopting a pragmatic and empirically sensitive approach to the use of forensic science and medicine, this essay defends Edmond and Roach’s “A Contextual Approach to the Admissibility of the State’s Forensic Science and Medical Evidence.” The authors reiterate their concerns about idealized approaches to science and expertise and question the utility of philosophically-driven and essentialist models of science for legal practice. In detail the essay explains why privileging process over outcomes in the criminal process (and even perpetuating the dichotomy) is misguided.The authors affirm the importance of factual accuracy and the socio-institutional illegitimacy generated by wrongful convictions. Drawing upon recent inquiries and recommendations which confirm their concerns about endemic problems confronting contemporary forensic science and medicine — especially around the limited research base — and noting the demonstrated frailties of accusatorial trials and appeals, the authors continue to advocate “demonstrable reliability” as an admissibility prophylactic.
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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 | 0.148 | 0.232 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.149 |
| Scholarly communication | 0.024 | 0.038 |
| Open science | 0.011 | 0.017 |
| Research integrity | 0.075 | 0.106 |
| 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".