Rethinking Online Privacy in Canada: Commentary on Voltage Pictures v. John and Jane Doe
Bibliographic record
Abstract
This article examines the Voltage decision, with the view that the bona fide standard safeguards intellectual property rights at the cost of online privacy rights and will proceed in three parts. Part I provides a brief contextualization of the issues. Part II is an analysis of the Voltage decision. Part III examines how the bona fide standard is a relatively low threshold. This article concludes by considering the possibility of shifting to a higher standard for disclosure, as well as a possible solution for the effect that a higher standard could have on copyright owners.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.053 | 0.026 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.062 | 0.059 |
| Insufficient payload (model declined to judge) | 0.006 | 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".