Facebook Fatalities: Students, Social Networking, and the First Amendment
Bibliographic record
Abstract
how kids behave." 9Indeed, while the school administrators were criticized in Prince, these same administrators are also frequently the target of similar vicious cyber attacks.In one recent case, a fourteen-year-old eighth grader at Blue Mountain Middle School created a fictitious profile of her principal that included his photograph from the school"s website, as well as profanity-laced statements that he was a sex addict and pedophile. 10 In another case, a student in Pennsylvania created a website entitled "Teacher Sux." 11 The website described the student"s math teacher in obscene terms and included pictures of the teacher"s severed head dripping blood, a picture of her face morphing into Hitler, and a solicitation for funds to hire a hit man to kill her under the caption "Why Should She Die?" 12 On the eve of the anniversary of Phoebe Prince"s tragic death, the purpose of this Article is to look for clues to that "magic-bullet" and to try and craft a workable legal framework to assist students, parents, and school administrators in navigating the complex legal waters that surround the regulation of off-campus cyberspeech.Utilizing Supreme Court precedent in traditional First Amendment student speech cases, this Article examines the application of that traditional framework to cases involving cyberbullying.The vehicle for doing this will be to examine two recent Third Circuit cases that involve very similar facts but resulted in dramatically different outcomes: J.S. ex rel.Snyder v. Blue Mountain School District, 13 where the Court found that a school could discipline a student for harassing off-campus speech on a social networking site, and Layshock ex rel.Layshock v. Hermitage School District, 14 which found that a school could not discipline 9.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".