Bibliographic record
Abstract
Scientific evidence is crucial in a burgeoning number of litigated cases, legislative enactments, regulatory decisions, and scholarly arguments. Evaluating Scientific Evidence explores the question of what counts as scientific knowledge, a question that has become a focus of heated courtroom and scholarly debate, not only in the United States, but in other common law countries such as the United Kingdom, Canada and Australia. Controversies are rife over what is permissible use of genetic information, whether chemical exposure causes disease, whether future dangerousness of violent or sexual offenders can be predicted, whether such time-honored methods of criminal identification (such as microscopic hair analysis, for example) have any better foundation than ancient divination rituals, among other important topics. This book examines the process of evaluating scientific evidence in both civil and criminal contexts, and explains how decisions by nonscientists that embody scientific knowledge can be improved.
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 | 0.281 | 0.500 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.016 | 0.009 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.037 | 0.021 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.019 | 0.014 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".