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Record W4379058375 · doi:10.21083/ajote.v12i1.7077

Student-on-teacher violence in South Africa’s Tshwane South District of Gauteng Province: Voices of the victims

2023· article· en· W4379058375 on OpenAlexvenueno aff
Roy Venketsamy, Elaine Baxen, Zijing Hu

Bibliographic record

VenueAfrican Journal of Teacher Education · 2023
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsnot available
Fundersnot available
KeywordsHarmQualitative researchPsychologyPedagogySociologySocial psychologySocial science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.317
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2023
Admission routes1
Has abstractyes

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