Bibliographic record
Abstract
Scientific evidence plays an essential role in many areas of civil law. Technological developments have had a particular impact in the area of civil liability, where the sheer amount of technical expert evidence can hinder court proceedings. In addition to the cost and time required to hear it, the presentation of all this evidence can have a paralyzing effect on legal decision making. Limiting the introduction of scientific evidence into civil proceedings has therefore become a major issue in modern procedural law. The legal systems of North America have taken a novel approach to addressing this issue: the admissibility of scientific evidence is limited by having the reliability of the evidence assessed by the judge, who has effectively become a “gatekeeper of scientific evidence.” This article provides an overview of the implementation and appropriateness of this standard for reliability, nearly 25 years after it was established by Daubert in the United States and Mohan in Canada. This retrospective finds that this test of reliability fails to align with its intended objectives, as demonstrated by its excessive application in the United States and its relatively poor application in Canada. These phenomena can be attributed to the complexity of the standard; its flexibility, which allows arbitrary positions to exert undue influence; and the difficulty for judges untrained in science to apply the standard. More importantly, this test of reliability is based on an inaccurate legal perception of science, which impedes the functioning of the law. The question is then: how can this standard evolve? Based on a trend in Canadian law, legal efforts could be redirected toward applying the standard to those presenting scientific evidence at trial, rather than to the evidence itself; this redirection would require a larger rethinking of the adversarial system.
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 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 | 0.023 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".