Bibliographic record
Abstract
Much of Patrick Glenn’s contribution to comparative law took the form of macro-comparison. William Twining has offered important criticisms of the undue attention to micro-comparison in the Western comparative law tradition. He has encouraged the enterprise of constructing macro pictures of law in the world. Glenn’s work, especially Legal Traditions of the World, shows how macro-comparison can illustrate the way different legal traditions can operate in combinations and across national borders. This challenges the compartmentalized view of legal systems adopted by bodies like the World Bank. Glenn combines understanding of social culture and ideology with history and geography in his picture of law. All the same, micro-comparison has a significant contribution. Micro-comparison helps us to understand how legal systems really work. The robustness of macro-comparative analyses depends on their ability to be verified in the close examination of specific instances of legal systems that they are supposed to explain. Macro-comparison is typically an exercise of seeing patterns in specific instances and developing a theory that continues on that pattern. For most scholars, micro-comparison ensures feet are on the ground and the tools are the comparative trade are well honed before embarking on the macro-comparative enterprise.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".