Bibliographic record
Abstract
Doubts are gathering about the future of Chevron deference. Under sustained attack from the forces of judicial supremacy on matters of legal interpretation and shorn of one of its strongest defenders (the late Justice Antonin Scalia), Chevron seems to be on the ropes. I begin by outlining the reasons for doubts about Chevron’s continued vitality. Deference has been criticized by members of the Supreme Court of the United States, state supreme courts and prominent academics. However, these reasons for doubt should not be overstated, as close scrutiny of recent developments suggests that the most plausible response is a modification, rather than eradication, of Chevron deference. Indeed, critical analysis reveals that the anti-Chevron arguments are surprisingly weak, with their analytical and philosophical rigour generally inversely proportionate to their rhetorical force. Although predicting the future is always hazardous, I suggest that Chevron's prospects are probably better than is commonly assumed by contemporary commentators.
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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.025 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 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".