Comparative Analysis of Copyright Enforcement in the Cloud under U.S and Canadian Law: The Liability of Internet Intermediaries
Bibliographic record
Abstract
Through an empirical comparison between U.S and Canadian copyright law, this paper examines how lawmakers in both countries should deal with copyright liability issues in the cloud while maintaining a proper balance between content owners and Internet intermediaries. This paper proposes to answer this question throughout the study of the liability of Internet intermediaries. Drawing on copyright statutory provisions, case law and scholars articles, this paper examines the issue of online piracy, defines cloud computing and identifies the copyright liability issues posed by the cloud. It then compares U.S and Canadian copyright laws and discusses the new reform proposed in both countries in relation with the liability of Internet intermediaries. It concludes that new statutory reform might not be necessary except for clarification purposes. Indeed current copyright laws deal efficiently with copyright liability issues in the cloud while maintaining a proper balance between content owners and Internet intermediaries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".