Bibliographic record
Abstract
The exploitation of empirical methodologies has had a late start in law compared with other social sciences. Though there have been consistent calls/or the scientific study of law-related problems since the late 1800s. the main impetus to actually begin conducting sophisticated and useful empirical studies has come from outside the profession, starting mainly in the 1950s. Since then, a growing number of evidence-based studies of legal topics have appeared, some authored by those trained in the law, others by those trained in other disciplines, often as collaborative efforts, and occasionally by scholars trained in both the law and empirical methodology. Prominent subjects have been the behaviour of juries, procedural justice, case loads in specific court systems, judicial decision-making, the legal profession, the impact of law on society and trends in specific types of cases, especially medical malpractice and product liability suits. This fluorescence seems to have tailed off somewhat since the late 1980s. What follows is an informal, non-exhaustive look at empirical studies relating to the reform of civil procedure and the improvement of the administration of civil justice.
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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".