Cyber Bulling Among Learners in Higher Educational Institutions in Sub-Saharan Africa: Examining Challenges and Possible Mitigations
Bibliographic record
Abstract
Proliferation of technology in the form of internet, mobile phone and social media access and usage is exposing many youths to cyber bullying activities. Cyber bullying activities are viewed as negative consequences of growth and development in technology. Many of the victims of cyber bullying include those that have been trapped in the technology through obsessive and addictive behaviours. The study was conducted in order to understand cyber bullying in educational institutions in Sub- Saharan Africa. The study is guided by the following five objectives: understanding cyber bullying and its manifestations among learners in education institutions; explore contributing factors in education institutions; determine the prevalence of cyber bullying in education institutions; examine the effects of cyber bullying among learners in education institutions and determine ways of dealing with cyber bullying among learners in education institution. The study adopted a case study approach and involved 123 respondents with a response rate of 64% (n=192). A survey questionnaire was used to collect data. Resulting data was analysed using statistical package for social sciences (SPSS). Evidence suggests that cyber bullying has serious psychological harm on the victims some leading to suicidal thoughts and suicide, among others. The study concludes that the effects of cyber bullying are far reaching and devastating to the learners and the institutional safety as well. The study recommends that more research and awareness are needed in an effort to control this menace and make outreaching and learning institutions safe.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".