Polygyny, Partnership Concurrency, and HIV Transmission in Sub-Saharan Africa
Bibliographic record
Abstract
We study the relationship between polygyny and HIV infection using nationally representative survey data with linked serostatus information from 20 African countries. Our results indicate that junior wives in polygynous unions are more likely to be HIV positive than spouses of monogamous men, but also that HIV prevalence is lower in populations with more polygyny. With these results in mind, we investigate four explanations for the contrasting individual- and ecological-level associations. These relate to (1) the adverse selection of HIV-positive women into polygynous unions, (2) the sexual network structure characteristic of polygyny, (3) the relatively low coital frequency in conjugal dyads of polygynous marriages (coital dilution), and (4) the restricted access to sexual partners for younger men in populations where polygynous men presumably monopolize the women in their community (monopolizing polygynists). We find evidence for some of these mechanisms, and together they support the proposition that polygynous marriage systems impede the spread of HIV. We relate these results to the debate about partnership concurrency as a primary behavioral driver for the fast propagation of HIV in some parts of sub-Saharan Africa.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".