They Promise They Won't Be Evil . . . But Should Google Still Be Your Friend after R v Ward?
Bibliographic record
Abstract
We have a love-hate relationship with online services. We are increasingly dependent on internet service providers (ISPs) and online service providers (OSPs) both at home and at work: their services connect us to essential aspects of modern social life, and they help us to find information, store data, and access businesses of all kinds. And yet ISPs and OSPs also pose a threat to our privacy: they possess private information about their subscribers, and in many cases, they will make this information available to police without a warrant. Using the recent decision of the Ontario Court of Appeal in R v Ward as a launch pad, we examine weaknesses in existing law that facilitate state surveillance of our online activities and erode our privacy. As well, we introduce a new theoretical approach to identifying the meaning of internet privacy, which we develop and deploy in a concrete way to suggest how jurisprudence under s. 8 of the Canadian Charter of Rights and Freedoms should be developed in order to bring it in line with social expectations about privacy and internet use. The phenomenological approach we describe involves a relational notion of rights that acts as a counterpoint to traditional individualistic approaches.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.010 |
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".