A Plea to Reject the United States Supreme Court's Due-Process Review of Punitive Damages
Bibliographic record
Abstract
Because the audience and readers of this piece are not United States lawyers, I supply background and I paint with a broad brush. In short, the United States Supreme Court's use of the Due Process Clause for judicial tort reform of punitive damages was a serious mistake. On the nebulous due-process foundation, the Court built imprecise yet wrongheaded doctrine based on misguided policy justifications. Other common-law countries ought to learn from our blunders, above all not to repeat them. I wrote this for the Second International Symposium on the Law of Remedies sponsored by the University of Windsor and the University of Auckland. The Symposium was in Auckland, New Zealand, in November 2007. It will be published in 2008 in a book titled The Law of Remedies: New Directions in the Common Law edited by Jeff Berryman and Rick Bigwood. The footnotes are in Canadian, not Bluebook, form.
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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 | 0.023 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.023 | 0.033 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".