The Excessive Fines Clause in the Federal Courts: A Quarter-Century of Narrowing
Bibliographic record
Abstract
The Eighth Amendment prohibits “excessive fines,” but what exactly does “excessive” mean? The question has taken on some urgency in recent years as American legislatures have sharply increased the economic penalties associated with criminal convictions. In 1998, in United States v. Bajakajian, the Supreme Court for the first time established a test of sorts to determine whether an economic penalty is “excessive” in violation of the Eighth Amendment. The test was not without its ambiguities but offered some potentially robust protection against the rising tide of fines, fees, forfeiture, and restitution. However, the promise of Bajakajian has been undermined in the lower courts. This Article presents the first systematic analysis of how Bajakajian has been interpreted and applied by the federal circuit courts of appeals. The Article shows that, at practically every turn, the circuit courts have adopted narrowing interpretations of Bajakajian, which have largely negated the practical significance of the Eighth Amendment ban on excessive fines. Indeed, in some important respects, the circuit-court opinions more closely resemble the dissenting than the majority opinion in Bajakajian. The Article concludes with a consideration of what the Supreme Court might do in response to the circuit-court cases, from acquiescence to simple reaffirmation of Bajakajian to the development of an even more robust and easily enforceable approach to the Eighth Amendment right.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".