Examining the Interface between HIV/AIDS, Religion and Gender in Sub-Saharan Africa
Bibliographic record
Abstract
While issues of gender are critical to an understanding of the continuing spread of HIV/AIDS in sub-Saharan Africa, little attention has been paid to the interface between gender and religion in the context of the pandemic. Most explorations deal with gender and HIV/AIDS, gender and religion, or HIV/AIDS and religion, but seldom the interplay between the three. Given the increasing stronghold and growth of religion (in particular Islam and renewalist Christian faiths) in many parts of sub-Saharan Africa, the potential impact of religion in either curtailing or advancing the spread of HIV/AIDS needs closer attention. While recognizing the positive roles that faith communities can and do play in terms of HIV/AIDS education, prevention, treatment, care and support, this article focuses primarily on how certain religious teachings and practice within sub-Saharan Africa may also inadvertently contribute to both the general spread of HIV/AIDS and the differential vulnerability of women and girls, boys and men to the virus. Specifically, this article examines the ways in which some faith-based teachings may reinforce gendered stereotypes and female subordination to male sexual demands, impact condom accessibility and usage, circumscribe the effectiveness of HIV/AIDS education programs, and contribute to a climate of stigma and discrimination, especially against women living with HIV/AIDS in the region.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".