Does Tort Law Deter Individuals? A Behavioral Science Study
Bibliographic record
Abstract
For nearly four decades, economic analysis has dominated academic discussion of tort law. Courts also have paid increasing attention to the potential deterrent effects of their tort decisions. But at the center of each economic model and projection of cost and benefit lies a widely accepted but grossly undertested assumption that tort liability in fact deters tortious conduct. This article reports the results of a behavioral science study that tests this assumption as it applies to individual conduct. Surveying over 700 first‐year law students, the study presented a series of vignettes, asking subjects to rate the likelihood that they would engage in a variety of potentially tortious behaviors under different legal conditions. Students were randomly assigned one of four surveys, which differed only in the legal rules applicable to the vignettes. In summary, the study found that although the threat of potential criminal sanctions had a large and statistically significant effect on subjects' stated willingness to engage in risky behavior, the threat of potential tort liability did not. These findings call into question widely accepted notions about the very foundations of tort law.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".