Bibliographic record
Abstract
Traditional bullying is not a new phenomenon, and this behaviour has plagued many countries. It has received a wide interpretation by many academics seeking to define this concept. Bullying itself may take various forms and it has developed and adapted to changing times throughout history. One such adaptation arose owing to the expansion of the Internet – in the form of online bullying or cyberbullying. Although bullying has been given various definitions, cyberbullying in South Africa has not been given a legal definition, and perpetrators are therefore not disciplined in the appropriate manner. Introducing a legal definition would assist law enforcement to regulate cyberbullying and protect victims from harm. Advances in technology have created many opportunities for people to communicate across the world. At the same time, they have also created unintended consequences, such as allowing online users to harm each other. Although bullying has existed for a long time, the threat of cyberbullying online is arguably worse than its traditional counterpart. Scholars have attempted to provide definitions of cyberbullying. However, there is confusion and there are contradictory views regarding the characteristics of this conduct. Recommendations are made on what the requirements should be for a legal definition of cyberbullying. A comparative analysis of the United States and Canada is relied upon to establish clarity on this conduct.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".