What newspapers tell us about teacher-on-learner violence in South African schools
Bibliographic record
Abstract
This small-scale qualitative study of newspapers’ portrayal of teacher-on-learner violence in South African schools is informed, firstly, by the World Health Organisation’s definition of violence and four of the World Health Organisation’s six main types of violence involving children and, secondly, by John Galtung’s theory of violence. South African media was used as a database for identifying South African English newspaper articles on teacher-on-learner violence. Qualitative content analysis was employed to systematically work through the identified newspaper articles. Five types of direct teacher-on-learner violence were identified: (1) The physical abuse of learners under the pretext of addressing learner misbehaviour; (2) the normalisation of the sexual abuse of learners by their teachers; (3) teachers’ use of words to systematically humiliate and tear down learners; (4) teachers’ negative stereotyping and discrimination of learners belonging to a different race; and (5) teachers’ malicious neglect of their in loco parentis responsibilities. Looking at teacher-on-learner violence through the lens of Galtung’s theory of violence, this study facilitates an understanding of the multi-layered and complex nature of teacher-on-learner violence and contributes to the existing body of knowledge on teacher-on-learner violence.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".