Judicial Policymaking in Published and Unpublished Decisions: The Case ofEnvironmental
Bibliographic record
Abstract
While recent research has improved dramatically our understanding of appellate judicial behavior in constitutional and criminal law, we know comparatively little about the majority of the decisions made by the fed-eral judiciary: civil case decisions in federal district courts. Moreover, by relying upon published cases exclusively, this research may misrepresent those forces influencing the majority of judicial decisions. We address these shortcomings by outlining an integrated model of judicial policymaking and using this model to explain civil penalty severity in all environmental protection cases (published and unpublished) concluded in federal district courts from 1974-91. Additive and interactive heteroskedastic unit effect regression models demonstrate that penalty severity in environmental cases is affected by case and defendant charac-teristics, judicial policy preferences, the surrounding political context, and federal institutional actors. These models also demonstrate that po-litical considerations are especially influential in published case decisions. Over the past quarter century, scholars have successfully uncovered many systematic factors underlying court decisions. The more we learn about judi-cial behavior, the more this behavior resembles that of other more traditional policymakers in the American political system. Upon reflection, this conclu-sion should not be surprising. Judicial decisions are policy decisions in that they allocate resources and values, and contribute significantly to the attain-ment of policy goals. While substantial bodies of evidence demonstrate that in arriving at their decisions judges consider traditional criteria (e.g., prece-
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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 | 0.127 | 0.322 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".