Bibliographic record
Abstract
“Adversarial” is a term used to describe the legal system in jurisdictions such as the United States, the United Kingdom, Canada, Australia, and New Zealand, all of which share the heritage of English common law. It refers to the control by the parties over the selection of legal theories and presentation of evidence. The hallmark of an adversarial system of justice is two competing sides, generally represented by lawyers, with a neutral judge serving essentially in the role of referee and legal decision‐maker. The relevant contrast with adversarial systems is with legal systems in which greater control is vested in state officials to control the presentation of facts and the legal contentions to be considered. These systems are rooted in Roman law of antiquity, as opposed to English common law, and are often referred to as civil law jurisdictions. This discussion will avoid the ambiguous term civil law, however, because it suggests the distinction between civil and criminal adjudication – that is, between cases in which a right asserted is personal to one of the parties (civil) and those in which a wrongdoer is prosecuted on behalf of the citizenry as a whole (criminal). Instead, legal systems founded in Roman law will be called Continental, to emphasize their development in the legal traditions of Continental Europe, particularly France and Germany.
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".