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Record W2560020848

Content Providers’ Secondary Liability: A Social Network Perspective

2016· article· en· W2560020848 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicLaw, AI, and Intellectual Property
Canadian institutionsnot available
Fundersnot available
KeywordsTortLiabilityDamagesNoticeContext (archaeology)HarmBusinessCommon lawDirectivePublic relationsInternet privacyLaw and economicsLawPolitical scienceSociologyComputer science
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.963
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.047
GPT teacher head0.238
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations7
Published2016
Admission routes1
Has abstractyes

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