Facebook Fatalities: Students, Social Networking, and the First Amendment
Bibliographic record
Abstract
FACEBOOK FATALITIES183 sixth birthday on February 4, 2010 and announced at that time that it had over 400 million members, making it the equivalent of the world"s third largest country, ahead of industrial countries such as the United States (308 million), Russia (141 million), and Japan (127 million).Indeed, Facebook"s population only trailed China (1.34 billion) and India (1.2 billion).4 The rate of growth for Facebook has been exponential, with approximately 700,000 new users a day and 21 million new users per month.5 At this rate, Facebook will soon be larger than any other country in the world.6 This explosive growth in social networking impacts all segments of society, but given the youthful nature of many Facebook users (54.3 percent of total users are ages eighteen to twenty-four), 7 the impact on students is dramatic and occasionally tragic.Phoebe Prince was not the first teen suicide victim of cyberbullying; there have been numerous other documented instances and they seem to be on the rise.8 Because these attacks take place in the cyberworld, the traditional pupil disciplinary framework is ill-suited to deal with this behavior.As the South Hadley School Superintendent noted in response to the suicide: "I think the principal did everything he could. . . .Everyone expects the schools to solve these problems, but we don"t have magic-bullet solutions to 4. Pam Dyer, The Facebook Juggernaut: Exponential Growth + World"s Leading News Reader?, PAMORAMA, (Feb.10,
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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".