Management of the Hatred of Schooling by Public Secondary School Pupils
Bibliographic record
Abstract
This paper evaluates the cause of enmity and animosity displayed by pupils to their teachers through their hatred of schooling in myriad public educational institutions in South Africa. The paper was motivated by diverse discourses about pupil-animosity, most of which were based on the perspective of parents as members of the society. This paper is conceptual and empirical within the qualitative research paradigm. The question guiding this paper is: to what extent is the suppression of learner-desires and choices contribute to the snubbing of schooling? Narrative enquiry and interviewing techniques were used to collect data. Out of the population of 14 secondary schools in one of the circuits in Capricorn district in Limpopo Province, 6 were conveniently sampled. In each of the 6 sampled secondary schools, only Deputy Chairpersons of the School Governing Bodies became research participants. Findings revealed that hatred of schooling could be ascribed to pupils viewing schooling as an inconvenience. Secondly, failing to teach according to learners’ preferred teaching strategies. Thirdly, content delivery to pupils being alien. Fourthly, schooling that obstructs learner-hedonism. Fifthly, schooling that is naturally highly regimented. Lastly, schooling that suppresses learner-voice. The researcher recommends for schooling to close the generational gap between pupils and teachers. Furthermore, future schooling has to be conducted through virtual classrooms other than the tiresome face to face contact.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".