How to Manage Issues Relating to the Use of Trial Experts: Standards for the Introduction of Expert Testimony through Judicial “Gate-Keeping” and Scientific Verification
Bibliographic record
Abstract
This paper considers issues relating to the introduction of expert testimony in both civil and criminal cases in the American legal system. The paper reviews the Daubert Rule and the standards governing the admissibility of expert testimony in relation to the requirements of peer review and the use of a recognized “scientific method.” A number of federal cases will be considered which have attempted to refine and apply the Daubert Rule in a variety of factual circumstances where expert witnesses have been called on to offer their testimony and opinions at various stages of the legal process.
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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 | 0.569 | 0.584 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.012 | 0.032 |
| Scholarly communication | 0.029 | 0.028 |
| Open science | 0.012 | 0.011 |
| Research integrity | 0.033 | 0.029 |
| Insufficient payload (model declined to judge) | 0.002 | 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".