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 – 1.1.1 Romanists judges – 1.1.2 Islamic 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 Quebec example (precedent v. jurisprudence) – 1.3.2 Civil 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 – 2.1.1 Judges-arbitrators: Conciliatory proceedings in customary law – 2.1.2 Judges-spectators & Judgesactors: Accusatory and Inquisitorial proceedings in common and civil law – 2.1.3 Half-judges: Effectivity problems in civil law systems – 2.2 The allocation of conflicts: A divisible competence – 2.2.1 The
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".