<em>Chevron</em> Abroad
Bibliographic record
Abstract
This Article presents our comparative findings of how courts in five other countries review agency statutory interpretation. These comparisons permit us to understand and participate better in current debates about the increasingly controversial Chevron doctrine in American law, whereby courts defer to reasonable agency interpretations of statutes that an agency administers. Those debates concern, among other things, Chevron’s purported inevitability, functioning, and normative propriety. Our inquiry into judicial review in Germany, Italy, the United Kingdom, Canada, and Australia provides useful and unexpected findings. Chevron, contrary to some scholars’ views, is not inevitable because only one of these countries has something analogous to Chevron. Indeed, one country has expressly rejected Chevron in dicta. Nevertheless, all but one or two of the countries (depending how one counts) have at least some limited space for deference to agency statutory interpretations. We do not call for American law to wholesale adopt any particular country’s form of judicial review. But our comparative study provides useful suggestions for improving Chevron’s overall functioning and for better grounding it on its theoretical foundations.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".