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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".