DIRECT AND VICARIOUS LIABILITY FOR TORT CLAIMS INVOLVING VIOLATION OF PRIVACY
Bibliographic record
Abstract
The growth of actions for violation of privacy presents a significant risk for defendants and an opportunity for civil claims to provide a mechanism for accountability. However, several key issues that would determine the scope of liability remain unsettled. In most cases, courts have concluded that the existence of statutes dealing with personal information does not exclude the possibility of civil actions, which is important given the limits of statutory remedies. Negligence claims in this context may face issues regarding the duty of care, particularly where the defendant is a public authority, and proof of injury, given that recovery for harms such as stress or economic loss is limited. Therefore, the availability of statutory or common law privacy torts, which do not require proof of actual damage, is very important, but the elements of these torts are evolving and may be difficult to prove against an organization where the main perpetrator of the violation is an individual employee or third party. Vicarious liability for a breach of privacy by a “rogue” employee is possible, but will depend on whether the facts show that the employer organization materially increased the risk of the violation. The current state of the law raises questions about the ability of these claims to effectively provide compensation or deterrence, but in the absence of legislative reform, the progressive development of the law on some of these issues could help to clarify and expand the options available to address ongoing threats to privacy.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".