Reducing Violence Through Chess: Involving Pre-service Teachers in Participatory Action in Schools
Bibliographic record
Abstract
In this article, I critically reflect and describe how our Chess Development Project (CDP), initiated by pre-service teachers at a university in South Africa, helped create a safer, non-violent, and more supportive school environment. This participatory action research (PAR) project highlights the idea that higher education institutions should not only add to knowledge production but also contribute to practical improvement in the community. In this project, we introduced one sports code, chess, in a township school to reduce violence. The research question addressed was: To what extent does learner engagement in the CDP by pre-service teachers reduce school violence and bridge the gap between the university and the school? We set out to explore and discuss the potential of using chess as an educational tool for creating awareness, improving knowledge, and changing attitudes towards the challenges the learners face in life. The data collection techniques used were field notes, student assistant notes, interviews, questionnaires, and transcribed recordings of our research team reflections. As the coordinator of the project, I played the role of facilitator between the pre-service teachers and the township school. The findings suggest that this project was transformative. As a collective, the students and I became more aware of the day-to-day challenges that schools and communities face in the township areas in South Africa.
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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 | 0.049 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.023 | 0.042 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".