Internet Intermediaries’ Liability: A North American Perspective
Bibliographic record
Abstract
The Brazilian Internet Bill of Rights established a brand new framework for Internet intermediaries’ liability regarding third parties’ content and activities. As explained in the previous chapter, the new Act provides for generous legal safe harbours to the benefit of Internet access providers and Internet application providers while also framing two derogatory regimes for revenge porn and copyright. This chapter compares the Internet Bill of Rights with both Canadian and U.S. frameworks and establishes that the Brazilian federal legislator is not the first to set different frameworks for varying matters, such as revenge porn and copyright. As the liability scheme for copyright infringement has yet to be designed, a comparison with Canada and the United States is particularly of interest. Indeed, the two North American jurisdictions have adopted different approaches to the matter. This chapter argues that Brazil should frame the upcoming copyright scheme following Canada’s notice-and-notice approach, considering it is the only one to be consistent with principles set by the Brazilian Internet Bill of Rights. As such, this chapter will only focus on legal frameworks advanced by statutes and case law. It should be borne in mind that the discussed provisions, while designing safe harbours for intermediaries, doesn’t render them mandatory. Certainly, access and applications providers are free to provide for other mechanisms through their terms of use, notably to streamline their process across jurisdictions. It is worth clarifying that the chapter will only consider intermediaries’ liability with respect to the content of third parties – also known as “user-generated content” –, i.e. content they didn’t directly author or actively contribute to.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".