Bibliographic record
Abstract
Abstract In law, duty to warn states that a party may be held liable for damages sustained to a second party, if the first party had opportunity to warn the second, and failed to do so. This concept was applied to mental health practitioners in the landmark California Supreme Court case,Tarasoff v. Regents(hereafter known asTarasoff I), which established the duty of a therapist to warn foreseeable victims of patients. In the rehearing of Tarasoff I in 1976 (hereafter known asTarasoff), the court clarified that the duty was to protect, with warning, a possible victim being one way of discharging this duty. For the past 30 years, both therapists and courts have wrestled with the implications of the Tarasoff duty. The majority of states have adopted Tarasoff‐like statutes, with various interpretations and limitations. Canada and the United Kingdom have also considered Tarasoff‐like statues, but to date, have mostly relied on an unlegislated concept that public protection occasionally supersedes patient confidentiality. This article examines the history of duty to protect, before and after Tarasoff, controversies surrounding this duty, and current trends.
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.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.043 | 0.010 |
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".