Bibliographic record
Abstract
This essay uses Justice Scalia’s and Breyer’s dueling opinions in FCC v. Fox Television Stations, Inc. (2009), as a vehicle for exploring the contested relationship between politics and policy change in administrative law. In Fox, a five – justice majority led by Justice Scalia insisted that an agency’s abandonment of an old policy position in favor of a new one should survive review for arbitrariness so long as the agency explains why its new position is reasonable. A different five – justice majority (yes – that adds up to ten) led by Justice Breyer thought that Justice Scalia’s stance left too much room for politicization of policymaking. To curb such influence, Justice Breyer insisted that an agency, to justify abandoning an old policy, must explain why it was reasonable to change from its old policy to the new one. Neither of these two approaches in Fox hits quite the right note. Justice Scalia’s view unduly minimizes the problem of politicization. Justice Breyer’s solution seems formalistic and easy to evade. A better way forward may lie in combining Justice Scalia’s simpler framework with Justice Breyer’s more suspicious attitude. Taking a cue from Justice Frankfurter in Universal Camera, the courts should respond to the potential for excessive politicization of agency policymaking not with more doctrinal metaphysics but with a suspicious “mood.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.029 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".