Judicial Systems in the World – Judicial Portraits: A study through time and space
Bibliographic record
Abstract
Introduction -1.Judges and the law: Between interpretation and creation of the rule -1.1 Judges-interpretors: Law from outside -1.1.1Romanists judges -1.1.2Islamic judges -1.2 Judgeslegislators: Law from inside -1.2.1 Customary judges -1.2.2 Common law judges -1.3 Judges-Janus: The double face of mixity -1.3.1 Civil law and common law: The Québec example (precedent v. jurisprudence) -1.3.2Civil law and Islamic law: The Egyptian example (the principle of legality of offences and sentences v. uncodified Islamic law) -2.Judges and conflicts: Between arbitration and authoritative resolution -2.1 Procedural logics: A progressive scale -2.1.1Judges-arbitrators: Conciliatory proceedings in customary law -2.1.2Judges-spectators & Judgesactors: Accusatory and Inquisitorial proceedings in common and civil law -2.1.3Half-judges: Effectivity problems in civil law systems -2.2The allocation of conflicts: A divisible competence -2.2.1 The
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".