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Does Tort Law Deter Individuals? A Behavioral Science Study

2012· article· en· W2153327433 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Empirical Legal Studies · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTortSanctionsVariety (cybernetics)LiabilityStrict liabilityJoint and several liabilityTort reformLawEconomicsPolitical sciencePsychologyActuarial scienceLaw and economics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.131
GPT teacher head0.360
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it