The Greening of Canadian Cyber Laws: What Environmental Law Can Teach and Cyber Law Can Learn
Bibliographic record
Abstract
This article examines whether Canadian environmental law and policy could serve as a model for cyber crime regulation. A wide variety of offences are now committed through digital technologies, including thievery, identity theft, fraud, the misdirection of communications, intellectual property theft, espionage, system disruption, the destruction of data, money laundering, hacktivism, and terrorism, among others. The focus of this Article is on the problem of data security breaches, which target businesses and consumers. Following the Introduction, Part I provides an overview of the parallels that can be drawn between threats in the natural environment and on the Internet. Both disciplines have innate characteristics that make them difficult to regulate, and which set them apart from other subjects. Part II looks at the current situation of cyber crime threats in Canada, as well as the Canadian government's regulatory response. It then goes on to trace the history of Canadian environmental law, as well as the many successes and failures that have been achieved in law and policy in this area. Indeed, policy-makers can learn a great deal from these efforts about the kinds of laws and policies that might be workable in the cyber-realm. Following this, Part III examines what specifically cyber-law theorists and policy-makers can learn from those in the environmental law field so as to move down a more appropriate, and effective, regulatory path.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.032 | 0.046 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".