Empty Ritual, Mechanical Exercise or the Discipline of Deference?: Revisiting the Standard of Review in Administrative Law
Bibliographic record
Abstract
The standard of review doctrine in administrative law raises a deceptively simple question - on what grounds may a court interfere with the decision of an administrative body? The framework for determining the degree of curial deference appropriate for particular administrative settings remains as elusive and critical a question today as ever.The evolution of the standard of review is in part a story of a maturing court and in part a story of increasing complexity of the administrative state. In this examination of recent developments in the standard of review jurisprudence of the Supreme Court, focusing first on the development of the pragmatic and functional approach as summarized in Ryan and Dr. Q., and second on the application of that standard in the Retired Judges case, this paper attempts to show how the court's evolving sophistication in its standard of review analysis must now come to terms with the operational complexity of administrative decision-making. While the goals of analytic rigor and transparency in the judicial determination of what degree of deference is appropriate are laudable aspirations, this paper argues that they ought to be accompanied by a fuller understanding of administrative realities and perspectives.
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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 | 0.120 | 0.238 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.008 | 0.096 |
| Scholarly communication | 0.025 | 0.028 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.001 | 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".