Misusing the "No Duty" Doctrine in Tort Decisions: Following the Restatement (Third) of Torts Would Yield Better Decisions
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.
Bibliographic record
Abstract
Focusing on a recent California Supreme Court decision, Verdugo v. Target Corp., theauthor analyzes the “no duty” doctrine and its improper use in recent tort decisions. Heargues that too many US appellate courts are misapplying the “no duty” doctrine by usingit in situations in which they are actually deciding whether there has been a breach of theduty of care. The author places recent applications of the “no duty” doctrine in the contextof recommendations made by the American Law Institute, and suggests that the case lawwould benefit if the courts reflected upon the relative roles of judges and juries and followedthe guidance of the Restatement (Third) of Torts.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it