Bibliographic record
Abstract
Gordon Tullock, trained in the law at the University of Chicago, was an implacable critic of the common law, which originated in England centuries ago and was transplanted to the United States, Canada, Australia, and other English-speaking nations during colonial times.1 Tullock generally favored legal regimes based on civil law or "Roman civil law," the latter being the phrase used by John Henry Merryman and Rogelio Pérez-Perdomo ([1969] 2007) to solve a terminological problem in the comparative legal studies literature.2 Although applications of that regime differ from place to place and have evolved over time (as, of course, has the common law), Tullock's preference is shared by most of the rest of the world, including Europe, many parts of Asia and Africa, all of Latin America, and "a few enclaves in the common law world (Louisiana, Quebec, and Puerto Rico)" (Merryman and Pérez-Perdomo [1969] 2007, 3).3 The civil-law tradition can eb traced to Rome, Germany, and France; the latter nation's Napoleonic Code of 1804 is its archetype (Merryman and Pérez-Perdomo [1969] 2007, 10).4
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.039 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".