Strong Women, Dangerous Times: Gender and HIV/AIDS in Africa
Bibliographic record
Abstract
Introduction: Women and Vulnerability to HIV/AIDS in Africa Globalization, Sexuality, and HIV/AIDS in Africa Vulnerability of Women and Girls to HIV/AIDS in Rural South Sudan The Cultural Context of Womens and Girls Vulnerability to HIV/AIDS Infection in Thyolo and Mulanje Districts of Malawi The Risk of HIV Among Women in Malawi: The Case of Female Domestic Workers and Their Experiences with Sexual Violence Itinerant Male, Marriage, Family and Gender Relations in Matrilineal Southern Malawi: Lessons & Challenges for HIV/AIDS Programming Social and Cultural Predictors of HIV/AIDS Related Health and Preventive Behaviors in Kisumu District, Kenya Sociocultural factors: Norms of Masculinity and Femininity in a Context of HIV/AIDS in Mozambique Factors Affecting the Male-Female Differences in Condom Perception and Use in Rural Malawi Why the ABC Model for Prevention of Sexual Transmission of HIV Infection Has Been Opposed by Sub-Saharan Africans for Decades Use of Cotrimozaxole Prophylaxis at First Contact with Medical Doctor at a Tertiary HIV Clinic in Harare Sexuality and the Culture of Silence in the Face of HIV/AIDS in East Africa: A Popular Culture Approach HIV/AIDS Art and Popular Culture in South Africa: Examination of Community Murals, Billboard Campaigns and Graffiti Art Religion and the Rights of African Women in the Age of HIV/AIDS: Illustrations from Kenya We Must Do Whatever It Takes: Promoting and Sustaining Black Canadian Womens Health Index.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".