Understanding human sexual networks: a critique of the promiscuity paradigm
Bibliographic record
Abstract
Increases in sexually transmitted infection (STI) and HIV rates worldwide have prompted the dedication of research to identifying transmission co-factors, with one such co-factor being an individual's number of different sexual partners. Currently, the majority of STI/HIV transmission models are based on the assumptions that sexual networks have random distributions; whereas, in real-life, these assumptions have proven incorrect because group norms produce variations in sexual practices and differences in transmission co-factors (i.e. number, type, and timing of sexual contacts, use of protection, and genital co-infections). In fact, sexual groupings follow the distribution of the scale-free network. Because human sexual assemblages form scale-free networks, a large number of sexual partners does not necessarily mean that an individual is at risk for acquiring an STI, or conversely, that a small number of partners means that an individual is not at risk. Therefore, while an individual's number of sexual partners is important for population-based and case-management initiatives, it is impossible to determine group sexual norms, network location, and γ values at the individual level. This signifies that a reliance on individuals’ number of different sexual partners to determine their need for STI/HIV testing may be an unnecessarily invasive practice that negatively impacts on testing practices. Thus, it is important to be aware that it is not so much this number of contacts that is important, but rather what occurs during these connections at a network level, and how many concurrent connections exist across the group.
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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 | 0.026 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.064 |
| Scholarly communication | 0.011 | 0.031 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".