Bibliographic record
Abstract
Matthew Lewans’s and Adrian Vermeule’s books demonstrate the triumph of a Thayerian approach to administrative law, which sees legal interpretation as a shared enterprise, over a Diceyian one, which views it as the exclusive province of courts. Their historical, comparative, and analytic treatments expose the depth of the institutional settlement in the United States and Canada in which courts have come to accept that the functional reasons for legislative delegation to administrative agencies – some combination of the agencies’ greater expertise, specialization, capacity, flexibility, and accountability – also justify judicial deference. These demonstrations reveal how much of our accumulated legal developments resurgent Diceyian critics of the administrative state and administrative law seek to cast aside. While largely fellow travellers, these books hold different lessons about the scope and grounds of law. For Vermeule, the diminished place of courts amounts to a larger abnegation of law. But the retreat of courts entails a retreat of law only if one holds on to an identification of law with the courts. In contrast, for Lewans, legitimate judicial deference to agencies is conditioned on agencies complying with the rule-of-law values of fair process and public reason giving. As a result, for Lewans, judicial deference does not mark a diminution of law but, rather, an occasion for judicial enforcement of legal values that apply independently of administrative agencies. These books thus leave us to ask: does law diminish as courts recede or does it still inhere, perhaps even more urgently, in administrative bodies?
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.047 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 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".