Content Providers’ Secondary Liability: A Social Network Perspective
Why this work is in the frame
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Bibliographic record
Abstract
Recent technological developments allow Internet users to disseminate ideas to a large audience. These technological advances empower individuals and promote important social objectives. However, they also create a setting for speech-related torts, harm, and abuse. One legal path to deal with online defamation turns to the liability of online content providers who facilitate the harmful exchanges. The possibility of bringing them to remove defamatory content and collecting damages from them attracted a great deal of attention in scholarly work, court decisions, and regulations. Different countries established different legal regimes. The United States allows an extensive shield—an overall immunity, as it exempts the liability of content providers in speech torts. This policy is not adopted worldwide. The E.U. directive outlines a “notice-and-takedown” safe haven. Other countries, such as Canada, use common tort law practices. This Article criticizes all of these policy models for being either over or under inclusive. This Article makes the case for a context-specific regulatory regime. It identifies specific characteristics of different content providers with their own unique settings, which call for nuanced legal rules that shall provide an optimal liability regime. To that end, the Article sets forth an innovative taxonomy: it relies on sociological studies premised on network theory and analysis, which is neutral to technological advances. This framework distinguishes between different technological settings based on the strength of social ties formed in each context. The Article explains that the strength of such ties influences the social context of online interactions and flow of information. The strength of ties is the best tool for designing different liability regimes; such ties serve as a proxy for the severity of harm that defamatory online speech might cause, and the social norms that might mitigate or exacerbate speech-related harm. The proposed taxonomy makes it possible to apply a sociological analysis to legal policy and to outline modular rules for content providers’ liability at every juncture. This Article does so while taking into account basic principles of tort law, as well as freedom of speech, reputation, fairness, efficiency, and the importance of promoting innovation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it