Before You Host a Party Read This: Social Host Liability and the Decision in Childs v. Desormeaux
Bibliographic record
Abstract
Recent cases in Canada dealing with the legal liability of social hosts for the harm caused to third parties by their intoxicated guests have created an increasingly polemical public debate. In this Comment, the author argues that while the decision in Childs is supported by the particular facts of the case, the underlying reasoning is deeply problematic. First, the distinction the Court makes between commercial and social hosts is exaggerated, and that the differences that do exist should influence the standard of care required of social hosts rather than the existence of a duty. Second, the suggestion in Childs that social host liability will impose an inordinate burden on social hosts is simply not true. Finally, the assertion that social host liability will intrude on a guest's individual autonomy and personal choice, or diminish their individual responsibility, is deeply problematic. Such a claim invokes the decontextualized and individualized language of neo-liberalism to deal with an issue that government and non-government organizations alike treat as involving both individual and community responsibility.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.025 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".