Bibliographic record
Abstract
This article reviews the recently-enacted section 391 of the Criminal Code of Canada, which sets forth that “[e]veryone commits an offense who, by deceit, falsehood or other fraudulent means, knowingly obtains a trade secret or communicates or makes available a trade secret” and, also, sets forth a related offense for any third-party who obtains, communicates, or makes available a trade secret obtained in the aforementioned manner. By conducting a broader examination of Canadian trade secret and confidential information law, this article situates section 391 in the context of Canadian law. It then conducts a comparative analysis of Canadian law with its American counterpart, where trade secret law has assumed importance in terms of national security, and posits that section 391 imports American conceptions of national security into Canadian law. Thereafter, the article examines three relevant areas pertaining to the application of the recently-enacted section, in particular: the constitutionality of the law; the scope and challenges of its enforcement; and the utility and policy implications of the law.
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.029 | 0.012 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".