Balancing Legal Process with Scientific Expertise: Expert Witness Methodology in Five Nations and Suggestions for Reform of Post-<i>Daubert</i> U.S. Reliability Determinations
Bibliographic record
Abstract
In a recent article on science and the law, Susan Haack suggested that “we could learn something from the experiences of other nations that are equally technologically advanced, but have different . . . legal arrangements.” Her suggestion is both appropriate and timely, as the evidence mounts on the problems with the current judicial management of complex science.\nThis Article starts with a simple, related premise, that the proper balance of legal process and scientific expertise is not a uniquely American problem. If this is true, then we should, as Haack suggests, seek inspiration for reform in the varying methodologies of other nations. After beginning with a critical examination of the U.S. expert witness system, this Article discusses the handling of expert witnesses in multiple common law nations (Canada and the United Kingdom) and in multiple civil law nations (Germany and Japan). After examining those systems, this Article makes recommendations as to which methodologies, currently in use and tested in those nations, offer the most promise in fixing the weaknesses exposed in our system.\nBy reviewing the weaknesses in Daubert assessment of complex expert testimony, how other nations handle similar evidence, and how certain discrete areas of foreign law could address the weaknesses identified in the U.S. approach, this Article offers reform alternatives to assist judges in balancing the need for accuracy and reliability of the science presented in court with the need to maintain our traditions of legal process.
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.
How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".