NEITHER FISH NOR FOWL: ADMINISTRATIVE JUDGES IN THE MODERN ADMINISTRATIVE STATE
Bibliographic record
Abstract
This article examines the role of administrative adjudication in the United States constitutional system. It begins by noting that such adjudication fits uncomfortably within a system of divided powers. Administrative judges, including administrative law judges [ALJs] (who have the highest level of protection and status), are considerably more circumscribed than ordinary Article III judges. Indeed, administrative judges are usually housed in the agencies for which they decide cases, rather than in independent adjudicative bodies, and they do not always have the final say regarding the cases they decide. In many instances, the agency can appeal an adverse administrative judge’s decision directly to the head of the agency, and the agency head retains broad power to overrule the administrative judge’s determinations. In other words, the agency can substitute its judgment for that of the administrative judge regarding factual determinations, legal determinations, and policy choices. As a result, many administrative adjudicative structures involve difficult tradeoffs between independence, political control, and accountability. This article examines issues related to the status and power of administrative judges, as well as the constraints that have been imposed on administrative adjudicative authority, and explores whether those constraints continue to serve the purposes for which they were originally imposed.
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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 | 0.007 | 0.014 |
| 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 | 0.021 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".