Student-on-teacher violence in South Africa’s Tshwane South District of Gauteng Province: Voices of the victims
Bibliographic record
Abstract
School violence is a growing concern globally and schools in South Africa find it difficult to manage the problem of learner behaviour. Despite various educational policies and frameworks developed to prevent school violence, the system continues to fail teachers. Due to violence, schools are no longer safe places for teachers and learners. In South Africa, teachers are regularly exposed to physical violence and verbal attacks by learners. Recent media reports about teachers being attacked by learners clearly show an intent to inflict severe physical harm on the teacher. This qualitative study was conducted in one province in South Africa underpinned by the National School Safety Framework. This qualitative study aimed to understand the lived experience of teachers who had experienced violence against them by learners. The findings revealed that teachers are experiencing social, emotional and psychological trauma. Many teachers cannot perform their duties fruitfully and are constantly anxious to go to school. Emanating from this study, the following recommendations have been proposed: the Department of Education in South Africa should enforce its numerous policies and guidelines to protect teachers against violence. Social and psychological support services should be made available to teachers who have experienced violence. A stricter disciplinary code of conduct should be implemented in all schools for learners.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".