Expert Evidence Admissibility: From Rocky Highlands to Swampy Lowlands, via the Medical Standard of Care
Bibliographic record
Abstract
This article proposes an alternative research program for expert evidence law scholarship. The program takes a path that diverges from the majority of writing in this field, in two main ways. First, it eschews the field’s predominant epistemological stance. This stance — termed “epistemological rectitude” — primarily emphasizes fact-finding accuracy and rigorous admissibility or “gatekeeping” standards. Second, the proposed program adopts a narrower focus of inquiry than that usually taken: instead of experts in general, the program examines specific types of experts employed in specific types of cases to prove specific elements in dispute. Part I begins by presenting the current state of expert evidence law and highlighting the epistemological rectitude animating both case law and commentary. It then explains how epistemological rectitude elides the degree to which expert evidence law is highly pragmatic and contextual in its practical operation, and the problem that this entails for legal knowledge. To respond to this problem, the proposed program employs a context-driven method, presented at the end of Part II. Part III unpacks and defends this method by adopting a narrow focus: expert evidence on the medical standard of care in malpractice cases. This narrow focus is adopted to show: the limitations inherent to studying experts in general; the extent to which contextual differences matter to the law’s operation; and the knowledge to be gained by narrowing inquiries in this manner. The conclusion outlines in broad terms how the proposed program can be developed.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.062 | 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".