Weber and Legal Rule Evolution: The Closing of the Iron Cage?
Bibliographic record
Abstract
Institutionalists have emphasized the importance of law for the spread of bureaucracy and examined its effects; but they have not examined the evolution of law as an institution in its own right, particularly from a Weberian standpoint. In this paper, we investigate whether or not there is an inexorable proliferation and refinement of rational legal rules within a law, as we have found to be the case with bureaucratic rules. In other words, are the same tendencies toward proliferation and refinement associated with the ‘closing of the iron cage’ found in the context of legal rules? An examination of all sections of a regional water law over a 90-year period shows that the number of law sections and the text covered by the sections actually declines over time, through alternating phases of gradual expansion followed by rapid collapse; that is via punctuated equilibrium. Most of the expansion is due to revisions of existing sections, rather than to births of new sections. Poisson models of births and event history models of revisions show that the sources of the proliferation within the law are, in fact, some of the same ones anticipated by Weber: the interpretation of the law by the courts, changes in political parties, and shock events such as war. But, in contrast to Weberian predictions, the result of this evolutionary process appears to be a law that is smaller, tighter and more functionally differentiated.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".