Science on Law's Terms: Implications of Procedural Legitimacy on Scientific Evidence
Bibliographic record
Abstract
Scientific evidence is increasingly relied on in litigation. Discussions and debates aimed at enabling courts to make the best use scientific evidence are therefore critical. This thesis adds the perspective of procedural legitimacy to the science and law dialogue. Procedural Legitimacy is the concept that consistent adherence to legal procedure maintains the overall legitimacy of the legal system, and the validity of its outcomes. I argue that to maintain legitimate legal outcomes, procedural rules must be applied consistently and vigilantly to scientific evidence. This means that admissibility rules must be applied properly to scientific evidence, and that admitted scientific evidence must be duly scrutinized and weighed against the legal standard of proof. This ensures that the legal outcome will be based on valid legal facts. When the law is applied to those legal facts, litigants are legitimately bound by the judicial decision, despite the risk of factual inaccuracy.
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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 | 0.170 | 0.352 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.015 | 0.151 |
| Scholarly communication | 0.032 | 0.044 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.024 | 0.026 |
| Insufficient payload (model declined to judge) | 0.008 | 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".