Bibliographic record
Abstract
It is in the arena of toxic tort cases that the “battle of the experts” has been the most vicious, protracted, and well publicized, at least in the United States. In the United Kingdom, Canada, Australia, and New Zealand, there are few toxic tort cases brought and far less controversy about the use of experts. In civil law countries such as the Netherlands, where experts are appointed by the court on mutual agreement of the parties and prepare a joint report, disagreements are rarely aired in court. But in the United States, toxic torts have been a battleground about what counts as science in the courtroom, and this issue propelled both Daubert and Joiner into the U.S. Supreme Court. Because of long latency periods and symptoms common to many diseases, proving causation in toxic torts nearly always involves the use of scientific experts, and courts are often stymied by their gatekeeping responsibilities in this arena. The courts have particular difficulty with several major issues, including statistical analysis, the admissibility and evaluation of animal studies, the impact of cumulative studies, and the conflation of admissibility with sufficiency. The underlying reason that courts appear to founder in this area is that causation – an essential element for liability – is highly uncertain, scientifically speaking, and courts do not deal well with this uncertainty.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".