Intangible Justice? Intellectual Property Disputes and Canadian Small Claims Courts
Bibliographic record
Abstract
This article investigates the jurisdiction and institutional competence of Canadian small claims courts and tribunals with respect to complex claims, and in particular, intellectual property (IP) claims. Recent research points to an increase in these types of claims. A doctrinal analysis finds small claims bodies have wide jurisdiction over intellectual property infringement, contract, and licensing disputes. They can also rule on issues of validity, though they cannot affect registrations in the databases of the Canadian Intellectual Property Office. Remedies including damages, accountings, and the recovery of infringing goods are available in many provinces. As to their capacity, the article assesses three representative forums within the lens of organizational justice—the British Columbia Civil Resolution Tribunal, Ontario Small Claims Court, and Nova Scotia Small Claims Court—for how they assign claims, support decision-makers to reach “correct” decisions, and govern themselves to ensure quality. The BC and Ontario bodies fare well. Nova Scotia does not. The article assesses measures taken in the United States and United Kingdom to provide access to justice for IP small claims, and how the disparities in institutional readiness may impact Canadian small and medium-sized enterprises. The authors conclude by calling upon Canadian policymakers to better equip small claims bodies with the resources necessary to resolve IP claims.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.033 | 0.020 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".