Privacy in the Age of the Internet: Lawful Access Provisions and Access to ISP and OSP Subscriber Information
Bibliographic record
Abstract
Bill C-30 (the Protecting Children from Internet Predators Act) and the Protecting Canadians from Online Crime Act are two recent attempts by the Canadian government to create incentives for Internet Service Providers (ISPs) and Online Service Providers (OSPs) to disclose the subscriber information of Internet users to government agencies. In this article, the author argues that while such provisions may not violate section 8 of the Charter based on current judicial interpretation, they ought to be found unconstitutional. To date, the Supreme Court of Canada’s search and seizure jurisprudence uses a normative framework that does not distinguish between defining the right to privacy and justifying limitations to it. This approach is not consistent with that taken for other Charter rights. The recent decisions of the Supreme Court in R v. Spencer and R v. Fearon may signal a slight shift, but they do not go far enough. If courts defined privacy interests more broadly than under current law and required the government to justify restrictions on these interests under section 1, this would create a legal regime that achieves a better balance between competing privacy and security interests.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".