From discomfort to collaboration: Teachers screening cellphilms in a rural South African school
Bibliographic record
Abstract
South Africa continues to contend with an HIV pandemic. Teachers have the potential to address prevention and treatment with their learners but they struggle to implement HIV and AIDS education. Cellphilm projects—using cellphones to create videos, and then screening these—is one example of how digital technology can be used to address barriers to teacher-implemented HIV education. In this article we focus on the work of nine teachers who screened their cellphilms to three youth audiences. We explore how teachers can integrate cellphilm screenings into their teaching practice to address HIV and AIDS, and we consider what this integration tells us about the potential and challenges of teachers dealing with this issue in rural South Africa. Informed by a framework of discomfort, we analyse participant observation notes, fieldnotes, and pre- and post-event interviews. We argue that moments of discomfort during the events reveal the difficulties and strategies that teachers use to negotiate multiple—sometimes contradictory—sexual health education policies. The cellphilm screening events provided an opportunity for teachers and youth to learn from each other, even as it contributed to a more nuanced response to the teaching that addresses HIV and AIDS.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.029 | 0.019 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| 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".