Implementing a Learners’ Code of Conduct for Positive Discipline in Schools
Bibliographic record
Abstract
Dealing with misbehaving learners remains a significant challenge for teachers in South African schools. Since the use of corporal punishment and other punitive measures in dealing with misbehaving learners is now illegal, alternative positive disciplinary measures have had to be put in place. There were nearly 11,600 cases of documented corporal punishment in schools across the country in 2019. In KwaZulu-Natal alone, the number of learners who experienced corporal punishment increased by three per cent, affecting a total of 226 372 learners between 2018 and 2019. The study reported on here, examined teachers’ and learners’ experiences in respect of the implementation of a learners’ code of conduct, to instil positive discipline in schools. Underpinned by the interpretivist paradigm, the study employed a qualitative research approach and phenomenological design. Two schools were sampled and, semi-structured interviews, observation and document reviews were used to collect data. The findings revealed that some teachers indeed implemented such a code of conduct, which communicated learners’ expected behaviour by outlining the rules and regulating behaviour. Notably, the findings also revealed that the code of conduct did not instil positive discipline across the board, as many learners continued misbehaving. Based on the findings, the study recommends that schools ensure that a proper code of conduct be drawn up to help teachers address learner indiscipline and that officials from the Department of Education undertake regular visits to schools, to offer support and arrange workshops/internet-based training to guide teachers on how to use such a code effectively.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".