Criminal Enforcement of Trade Secret Theft: Strategic Considerations for Canadian SMEs
Bibliographic record
Abstract
Many innovative small and medium enterprises (SMEs) face unique challenges in protecting their intellectual property (IP). Potential theft of trade secrets is a key feature of these challenges, which arises often in the context of disputes related to employee mobility. Despite the risks these challenges pose, SMEs often confront significant resource barriers in protecting themselves from trade secret theft. The passage of a recent criminal law by the Canadian federal government, section 391 of the Criminal Code, creates a powerful new tool for innovative SMEs to report, investigate, and prosecute theft of trade secrets. It also comes with specific considerations and risks that innovative SMEs should examine and contemplate. This article explores strategies for SMEs in Canada to use section 391 to protect their trade secrets, navigate the legal environment during theft of a trade secret, and remediate such theft.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.042 | 0.007 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".