Growing up an orphan: vulnerability of adolescent girls to HIV in Malawi
Bibliographic record
Abstract
Based on a qualitative study conducted in the township of Chibavi in Mzuzu City, Malawi, this paper seeks to contribute to the emerging debate as to why orphans may be more vulnerable to the AIDS epidemic through the lens of an informal labour relation locally known as ganyu. The paper argues that although ganyu has deep roots in the country's history and has served as an escape from extreme poverty in rural areas, its transition to the urban landscape is associated with an emergent practice of sexual exchange between those who seek ganyu and those who recruit the workers. While youth in Chibavi generally work ganyu, the particularly oversized domestic roles of encumbered orphans against a backdrop of extremely deprived material circumstances and weak kin ties propelled them into prolonged ganyu contracts and compelled them to more readily concede to sexual demands ‘imposed’ by those who offered them ganyu. Drawing on geographic perspectives from political ecology of health and tracing the historical and geographic interconnections of ganyu, this study adds to the understanding of how the spatial transformation of this enduring ad hoc labour makes it a relation that potentially shapes vulnerability to HIV in Malawi. This study also wrestles with the question of why current policy debates do not reflect these realities in a country with one of worst AIDS epidemics, and in turn, makes relevant policy recommendations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".