Cyber Bullying: Legal Obligations and
Bibliographic record
Abstract
On a seemingly normal Tuesday a¯ernoon, an eighth grade girl walks out of school and steps into her mother’s car, ashen and visibly shaken. Unsure of how to proceed, her mother waits-she does not ask, and she does not move the car. Finally, her daughter speaks, saying she received the following cyber Introduction 359 Bullying: Its Forms and Conditions 362 Cyber Bullying as an Extension of Bullying 363 Anonymity, Lack of Supervision, and an Innite Audience 365 Lack of Rules and Supervision 366 Prevalence of Sexual and Homophobic Harassment 368 Roles and Responsibilities: Schools or Parents? 369 Legal Obligations 370 e Educational Policy Vacuum 373 Freedom of Speech and Expression Rights 373 Student Privacy and Cyber Bullying 380 Tort Law and Negligence 382 Canadian Human Rights and U.S. Sexual Harassment and Discrimination Law 383 Conclusion and Implications 385 Policy Development 386 Research, Teacher Education, and Professional Development 387 Interactive Online Educational Programs 387 Student Empowerment and Critical inking 387 References 388 message during class: “Bitch, I know where you live. You’d better sleep each night with one eye open, on your knees. If you don’t . . . I’ll be there to be sure you do! —e Avenger.”
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".