A torrent of copyright infringement? Liability for BitTorrent file-sharers and file-sharing facilitators under current and proposed Canadian copyright law
Bibliographic record
Abstract
BitTorrent has become the primary means to share large files (movies, television shows, and music) over the internet. Canadian copyright law and jurisprudence have not kept pace with technology, and as a result there is no definitive pronouncement on the liability for copyright infringement of BitTorrent file-sharers, i.e. users, and file-sharing facilitators, i.e. Internet Service Providers (ISPs) and torrent search engines. Extrapolating from existing law and Canadian and foreign jurisprudence, I conclude that: (i) BitTorrent file-sharers are liable although there may be situations where fair dealing could apply; (ii) it may be possible to show ISPs are liable based on certain findings of fact; and (iii) torrent search engines should not be liable for infringement. There have been three successive attempts to reform copyright law that have addressed internet issues generally and file-sharing in particular. Under the most recent attempt, Bill C-32, file-sharers would be liable under the new "making available" right, and file-sharing facilitators could be liable under the new "enabling" concept of secondary infringement introduced with the bill.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".