Giving Life to Data: University—Community Partnerships in Addressing HIV and AIDS through Building Digital Archives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The partnerships, especially university–community partnerships, that sustain globally networked learning environments often face challenges in mobilizing research to empower local communities to effect change. This article examines these challenges by describing a university–community partnership involving researchers and graduate students in Canada and South Africa, working with a rural community in KwaZulu-Natal in South Africa in order to develop a participatory digital archive of more than 3000 photographs and videos collected from various visual methodology research projects related to HIV and AIDS education. The main purpose of the digital archive was to place community members as active participants at the centre of data analysis, as opposed to recipients of findings, and to give voice to teachers, learners, health-care workers and parents in identifying the key issues and challenges affecting their lives in the context of HIV/AIDS and their impact on their communities. The article outlines the technical and conceptual issues in developing the partnership as well as the digital archive, such as developing a scanning protocol, producing a metadata schema, choosing the digital archive software and, most importantly, involving community members, in particular teachers, in the processes of coding the visual data and using the archive for HIV and AIDS education and community change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it