Community Health Workers Working the Digital Archive: A Case for Looking at Participatory Archiving in Studying Stigma in the Context of HIV and AIDS
Bibliographic record
Abstract
Addressing the issue of HIV-stigma is recognised as essential to reducing the spread of HIV and AIDS, enabling community members to access prevention, treatment and care. Often the very people who are able to contribute to solving the problem, are marginalised and do not see ways to insert themselves into dialogues related to combating stigma. Community health workers in rural South Africa are one such group. At the heart of the research discussed in this article is an intervention based on participatory analysis through participatory archiving ( Shilton and Srinivasan 2008 ). Drawing on participatory work with thirteen community health workers in rural KwaZulu-Natal, we use a digital archive containing HIV-stigma visual data - generated five years earlier by youth in the community - to engage the participants in the analysis. Drawing on such participatory work as Jenkins’ participatory cultures framework, we focus on the idea of re-using, re-coding, and re-mixing visual data. One participant stated that “these pictures talk about the real issues faced by our communities”, highlighting the value of resources generated by community members themselves. They also indicate that they “could use [the resources] to teach the cons of stigmatising”. A key concern in work related to visual images (particularly in projects such as ours where a large amount of visual data is produced) is to consider ways of extending its life through the use of community-based digital archives.
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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 | 0.073 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.086 | 0.082 |
| Scholarly communication | 0.025 | 0.025 |
| Open science | 0.008 | 0.037 |
| Research integrity | 0.018 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 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".