By All Unlawful Means? An Inquiry into the Scope of the Unlawful Means Tort
Bibliographic record
Abstract
The nature, rationale, and scope of the tort of unlawful interference with economic relations, or ‘unlawful means’ for short, was recently clarified by the Supreme Court of Canada in the AI Enterprises v Bram Enterprises ruling. Justice Cromwell, writing for a unanimous Court, held that the definition of ‘unlawful means’ required to establish liability must be interpreted narrowly. Potentially tortious acts are therefore only unlawful if they are actionable by the third party or if they would have been actionable had they caused the third party a loss. Especially given its turbulent history, Justice Cromwell’s main concern throughout his opinion is to ensure the certainty and predictability of the tort’s application. In this respect, his approach is laudable. The author argues, however, that Justice Cromwell’s interpretation of the term ‘unlawful’ is simply too narrow and may have the effect of restricting otherwise valid plaintiff claims against defendants who have acted immorally, outrageously, and excessively. Instead of Justice Cromwell’s approach, the author proposes a broader interpretation that acts as an effective mechanism to capture malicious behaviour that might otherwise be non-actionable under the narrow definition. Importantly, it does so in a manner that maintains a high level of certainty and predictability in its application.
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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.055 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".