Digital inclusion for children in the social context of HIV/AIDS awareness
Bibliographic record
Abstract
Abstract Digital technology brings a new opportunity to develop education, culture, and knowledge in less‐developed communities. Researchers deal with issues related to digital inclusion for indigenous communities since it can provide people with the chance of better communication and faster access to information in all formats. A research team with the International Visual Methodology for Social Change Project initiated the AIDS prevention and awareness program and aimed to effectively serve as a form of social and digital inclusion in underprivileged communities in KwaZulu‐Natal, South Africa. The significance of this project is highlighted by adopting digital technology and the engagement of children, youth, and students in playing active roles in addressing HIV and AIDS through the photovoice research method. This method teaches children and youth how to document their lives and activities in schools and within the community through photography. They are then asked to describe what they see on the photos and to share their thoughts and feelings relating to issues surrounding HIV and AIDS. In this way, more than 3000 photos have been collected from the rural areas, taken by the children and youth of this region. To build a digital archive, the project went through the followings: photo selection, a scanning protocol, a database protocol in Greenstone software program, metadata protocol, and web site development. We described the entire visual data set based on Dublin Core. Two additional elements for captions were added, one for comments by librarians or metadata managers and the other for users' personal comments related to a specific picture to further enhance their participation in the project. Rural communities face challenges in HIV/AIDS health care and education, and access to HIV/AIDS information is essential for the disease caregivers. Working together and understanding the social effects of digital inclusion is a key area of our research.
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How this classification was reachedexpand
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".