Compensation for Non-Economic Loss, the Tort-Liability Insurance System, and the 21st Century
Bibliographic record
Abstract
Tort awards for non-economic loss have grown in an almost geometric progression in the United States since first recognized nearly 150 years ago. Not only have courts constantly expanded the areas in which claimants are permitted to recover pain and suffering awards, but at the same time they have liberalized the definition of pain and suffering itself This may be traced in part from the way the judicial system was designed after the American Revolution, the role of lawyers in the system, and the affluence of the country. Consequently, awards for non-economic loss have taken on ever increasing importance, an importance that does not bode well for the prospects for future adoptions of no-fault auto insurance plans that would curtail such recoveries. This article sketches historical influences on the tort-liability insurance system and summarizes modern developments in the law of damages for non-economic loss in the United States. It then raises questions regarding the prospects for adoption of the federal Choice No-Fault Auto Reform Act now pending in the U.S. Congress, a plan that would offer auto accident victims the choice of being compensated on a no-fault basis, while waiving their right to recover for pain and suffering. It concludes by offering a possible scenario of how future efforts to reform the tort-liability system in the United States may occur as we move into the 21st century.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".