Law & Leviathan: Redeeming the Administrative State by Cass R. Sunstein and Adrian Vermeule
Bibliographic record
Abstract
As the argument goes: Over the last hundred years or so, Congress has steadily delegated away its law-making responsibility through broad grants of rule-making and discretionary authority to an unelected and unaccountable federal bureaucracy. And the US Court, in decisions such as Chevron and Auer v Robbins, has similarly relinquished any right it once asserted to oversee the interpretation and performance of that delegated authority. On this reading, the sprawling federal administrative apparatus, which touches on virtually every aspect of American life, exists in contravention of the proper division of powers under the Constitution and is, therefore, not legitimate. In Law & Leviathan: Redeeming the Administrative State (“Law & Leviathan”), Cass R. Sunstein and Adrian Vermeule set out to confront this (in their view, exaggerated) narrative and to inspire some conservative confidence in the administrative state.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".