Cyberbullying: Should Schools choose between Safety and Privacy?
Bibliographic record
Abstract
In this theoretical article, we explore the tangled messiness of the application of human rights versus the 21st-century monster called "cyberbullying" in schools and focus on some of the challenges schools face daily. The research will reveal that cyberbullying victims were almost twice as likely to attempt suicide as youth who had not experienced cyberbullying, which implies that this is a phenomenon schools ought not to take lightly. We argue that everyone has a right to the freedom of expression, including in cyberspace, and begin by exploring how legal principles evolved in an attempt to deal with the limitations placed on an individual's right to freedom of expression. As we are about to reveal, though, matters become even more complicated when this freedom of expression relates to cyberspace, a space where users might have an expectation of privacy and even enjoy a state of anonymity. Clearly, the right to privacy and the right to freedom of expression need to be balanced and respected should school authorities be called upon to identify and discipline a cyberbully. This balancing act is one that needs to be investigated and carefully expounded upon, and is an issue that has not yet been sufficiently addressed in South Africa. Seeing that countries such as the United States of America and Canada have attempted to deal with this issue, it would be prudent to discuss the strides these countries have made, the challenges they have faced, and the insights they have gained, in an attempt to alert South Africa to the complex issues cyberbullying could raise. Working from this premise, this article will focus on the right to privacy, specifically in relation to Bill C-13 recently passed in Canada and the resultant Canadian Supreme Court decision in the case R v Spencer, a case that shed further light on the issue of privacy in cyberspace. We conclude the discussion by highlighting several potential pitfalls legislation such as Bill C-13 could create, and ask that constitutionally sound legal remedies be developed without delay to assist South African school governing bodies in the arduous task of having to deal with cyberbullying, to ensure that they are not faced with the question as to whether their learners' safety or privacy should come first.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.039 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 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".