Intentional Noise Exposure as a Battery? A Case Study of Canada’s Freedom Convoy
Bibliographic record
Abstract
This article assesses a hypothetical battery claim for noise exposure in Australia using Canada’s Freedom Convoy as a case study. I first advance a normative account for why battery ought to respond to noise- related interferences using Kit Barker’s taxonomy of ‘vindication events’. I argue that battery, relative to negligence and private nuisance: (1) more accurately ‘marks’ and ‘declares’ the plaintiff’s right to bodily integrity and ‘denounces’ the defendant’s intentional interference; and (2) improves access to ‘appropriate compensation’ post-infringement. I also explain how the ‘prevention of rights infringements’ fits within my normative account. I then answer the key doctrinal questions of whether and when noise exposure constitutes an actionable battery. I draw from existing common law precedents to show that physical contact by sound waves is ‘direct’ and capable of being ‘offensive’. I conclude by addressing the concern that my doctrinal conclusions would unduly burden protesters’ implied freedom of political communication.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".