Social Host Liability: A Logical Extension of Commercial Host Liability?
Bibliographic record
Abstract
This article explores whether social host liability should be recognized in Canada. There appears to be some reluctance to acknowledge social host liability. Although this may be a reflection of collective welfarism, it is inconsistent with negligence law generally and also a disincentive for accident prevention.There is no reason for social hosts to enjoy immunity from liability where they have failed to do what a reasonable person ought to have done in similar circumstances to prevent a foreseeable risk of injury. Profitability, which has traditionally been used to justify the imposition of liability on commercial hosts and not social hosts, is best considered in determining the appropriate standard of care and not the existence of a duty of care. The author examines decisions on social host liability; arguing that liability has not been imposed, not because it would be inconsistent with Canadian law, but because of failure to establish some essential requirements for negligence liability in the circumstances.Social host liability is a logical extension of commercial host liability; brings the law in line with negligence law generally, and encourages socially responsible behaviour on the part of social hosts.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".