R. v. Spencer and the Affirmation of Internet Privacy Rights in Canada
Bibliographic record
Abstract
opinion in R. v. Spencer (Spencer) holding that the Canadian public enjoys certain privacy rights and expectations in regard to their use of the Internet.1 It begins by presenting the background information and lower court rulings that led to this case the Supreme Court of Canada.Continuing, the article presents an analysis of the Supreme Court's ruling in Spencer.The last section of the article looks at developments in the Federal Parliament since Spencer, in particular, legislative proposals that most likely need to be modified in light of the Supreme Court's ruling. I. R. V. SPENCER: BACKGROUND AND LOWER COURT RULINGSBefore Spencer, police in Canada investigating crimes on the Internet traditionally would request information about an Internet user suspected of committing a crime from their internet service provider (ISP) via the use of general investigative police powers listed in section 487.014 of the Canadian Criminal Code (Criminal Code) or through requests to an ISP made via what is known as the "lawful authority exception" in section 7(3)(c.1)(ii) of the Personal Information Protection and Electronic Documents Act (PIPEDA).2 One such criminal case arising from a request made through the PIPEDA procedure ultimately reached the Supreme Court and led to the opinion in Spencer. 3
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.034 | 0.019 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".