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Enregistrement W2772447100

A New Method to Address Cyberbullying in the United States: The Application of a Notice-and-Takedown Model as a Restriction on Cyberbullying Speech

2017· article· en· W2772447100 sur OpenAlexaboutno aff
Brian O'Shea

Notice bibliographique

RevueFederal communications law journal · 2017
Typearticle
Langueen
DomaineComputer Science
ThématiqueHate Speech and Cyberbullying Detection
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésScrutinyNoticeLegislaturePolitical scienceLawDigital Millennium Copyright ActArgument (complex analysis)Government (linguistics)State (computer science)Law and economicsSociologyMedicineComputer scienceLinguisticsIntellectual property
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

TABLE OF CONTENTS I. INTRODUCTION 121 II. THE PROBLEM OF CYBERBULLYING AND THE NEED FOR A LEGAL SOLUTION 123 III. STATE RESPONSES TO CYBERBULLYING AND LEGISLATIVE SHORTCOMINGS 125 A. The United States' Response to Cyberbullying Has Occurred a the State Level at the State Level 126 B. Criticism of State Cyberbullying Responses and the Need for National Action 126 IV. THE RIGHT TO BE FORGOTTEN AS A POTENTIAL RESPONSE TO CYBERBULLYING AND WHY IT LIKELY WILL NOT SURVIVE FIRST AMENDMENT SCRUTINY IN THE UNITED STATES 127 A. The Right to be Forgotten, Criticisms of the Right, and Its Impact on Speech in the E.U. 128 B. The Right to be Forgotten, as Implemented in Europe, Would Face Serious First Amendment Challenges in the United States 129 1. Low-Value Speech Can Be Restricted by the Government with Minimal First Amendment Scrutiny 130 2. Restrictions on Speech That Is Not Low-Value Are Subject to Strict Scrutiny Under the First Amendment 131 C. Due to Its Chilling Effect on the Content of a Wide Range of Speech, the Right to Be Forgotten Is Not Likely to Survive Strict First Amendment Scrutiny in the United States 133 V. POLICYMAKERS SHOULD LOOK TO THE NOTICE-AND-TAKEDOWN PROCEDURES OF THE DIGITAL MILLENNIUM COPYRIGHT ACT, WHICH MAY PROVIDE A CONSTITUTIONAL MEANS FOR RESTRICTING THE CONTENT OF SPEECH 134 A. Background on the DMCA and Its Notice-and-Takedown Provisions 134 B. The Argument That the DMCA's Notice-and-Takedown Procedures Provide for a Potentially Unconstitutional Restriction of Speech 136 VI. APPLICATION OF THE DMCA NOTICE-AND-TAKEDOWN MECHANISM AS AN ALTERNATIVE MODEL TO RESTRICT THE CONTENT OF CYBERBULLYING SPEECH 137 A. The Elements of This Proposed Notice-and-Takedown Mechanism 138 B. Why This Mechanism Is a Constitutional Speech Restriction 139 C. Potential Counterarguments and the Need for Further Scholarship 141 1. Websites Already Have Protections in Place 141 2. The Need for an Appeals Process 142 VII. CONCLUSION 143 I. INTRODUCTION Ghyslain Raza. His story is one many may not want to remember--but should never forget. One day, while at school in Quebec, Canada, Raza was going about his day like any typical 14-year-old. He had countless things to look forward to: spending time with friends, high school, and enjoying what are supposed to be some of the best years of life. His teenage innocence, however, was about to be ripped away from him far too soon. As part of a school project, Raza entered a television studio at his school and had someone film him reenacting a lightsaber scene from Star Wars. Raza submitted the seemingly harmless and inconsequential video in his class and then went on with his life. (1) A year later, the video was posted on YouTube, without Raza's consent, and quickly went viral. Within days of its posting, the video was well on its way to becoming the most popular Internet video of all time. But rather than enjoying his newfound celebrity, Raza was faced with a massive cyberbullying onslaught from people he did not know. (2) What I saw was mean. It was violent. People were telling me to commit Raza said of the video's release. (3) Raza further commented that no matter how hard I tried to ignore the people telling me to commit suicide, I could not help but feel worthless, like my life was not worth living. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies, Communication savante
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Méthodes · Signal consensuel: aucune
Score de désaccord entre enseignants0,820
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0040,000
Communication savante0,0010,001
Science ouverte0,0040,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,053
Tête enseignante GPT0,354
Écart entre enseignants0,301 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2017
Routes d'admission1
Résumé présentoui

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