Children’s images of HIV/AIDS in Uganda: What visual methodologies can tell us about their knowledge and life circumstances
Bibliographic record
Abstract
Abstract In this study we draw on three analytic frameworks (Goffman 1981.Forms of talk. Philadelphia, PA: University of Pennsylvania Press; Rose 2007.Visual methodologies: An introduction to the interpretation of visual materials. London: Sage; Warburton 1998. Cartoons and teachers: Mediated visual images as data. In John Prosser (ed.),Image-based research: A sourcebook for qualitative researchers, 252–262. London: Routledge) to explore how multilingual children in a rural Ugandan primary school use visual and linguistic modes to create billboards messages about HIV/AIDS. Although HIV/AIDS education is required curriculum in public schools, and outside of the classroom students are exposed to various national public service announcements (e. g., on radio and television, and as billboards), there are still considerable cultural barriers that hinder open discussions between children and their teachers and parents about HIV/AIDS-related issues. Our findings suggest that communicating the complex language of HIV/AIDS prevention requires students in this cultural context to go beyond the linguistic mode and draw upon the visual in order to achieve a fuller range of socio-affective expression, and conceivably, to affect change by reaching a variety of audiences on multiple levels of human meaning making. Implications for literacy educators in multilingual contexts, where pressing social issues intersect with culturally sensitive or otherwise “unspeakable” topics, indicate that the visual offers a less institutionalized and culturally-laden space for children to synthesize the messages in their environments and their own relationship to them.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".