Women and power in the vulnerability to HIV infection: \nthe case of Malawi
Bibliographic record
Abstract
As the HIV/AIDS pandemic matures increasingly more women are infected than men. The heightened prevalence amongst women suggests that they are at particular risk of infection. Although the gendered dimensions of HIV/AIDS have been recognised, this is not fully understood and the tendency is to respond at a surface level only. This research provides a critique of the current response to HIV/AIDS in Malawi and a theoretically informed analysis of why women are vulnerable. It is argued that the response is limited because it fails to engage with the gendered dimensions; and that gendered structures of power underscore the vulnerability of women to infection. This research makes an important contribution to the feminist task of radically challenging the conventional boundaries of international relations, which typically draws upon a masculine form of knowledge. It also challenges the dominance of the scientific discourse that governs the way HIV/AIDS is conceived and makes an important contribution to the literature on understanding the gender context. A ‘feminist interprevist’ approach is employed and the methodology is a combination of a case study analysis, semi-structured key informant interviews, documentary analysis and data analysis. Feminist critical theory and post-structuralist understandings of how power operates through gendered structures provide the theoretical basis for the empirical analysis. The critique of the response to HIV/AIDS in Malawi reveals how it fails to engage with the gender power relations because of pervasive gendered structures. Three aspects of women’s vulnerability to HIV infection are explored: the majority of women are not in the position to negotiate for safer sex; many women do not have the power to leave a marriage if it puts them at risk of infection; and the biological susceptibility of women to infection. It is argued that gendered structures underscore this vulnerability. The gendered structures are deeply embedded and hard to discern. However, where HIV/AIDS travels along the fault lines of society it reveals these deeper structures of power and provides the opportunity to challenge them. It reveals the importance of empowering women across their lives in order for a sustainable and effective response to HIV/AIDS.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.018 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".