A New Method to Address Cyberbullying in the United States: The Application of a Notice-and-Takedown Model as a Restriction on Cyberbullying Speech
Bibliographic record
Abstract
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. …
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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 | 0.002 | 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 | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".