Two years later: preservice teachers' experiences of learning to use participatory visual methods to address the South African AIDS epidemic
Bibliographic record
Abstract
South Africa continues to struggle with the world's highest HIV rates, and the country's young people are amongst those most severely affected by this epidemic. The education sector, and especially teachers, are situated to be leaders in the national response and can provide emotional support as well as information on gender, sexuality, and HIV and AIDS. This article explores preservice teachers' experiences two years after participating in the Youth as Knowledge Producers (YAKP) research intervention, which provided them with training and practical experience in participatory visual methods for HIV and AIDS education. The article discusses participants' reflections on the methods, the continued influences of YAKP on how they think and approach teaching, and the barriers they experienced in securing further learning in this area. The research concludes that preservice teachers can benefit from short-term training in participatory visual methods for HIV and AIDS education by being exposed to a new pedagogical approach, and suggests further development in the integrated responses of higher education institutions in relation to the preservice teachers' HIV and AIDS education.
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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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.014 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".