Some Methodological Issues in the Study of Sexual Networks
Bibliographic record
Abstract
BACKGROUND: Mixing between sexual activity classes is an important determinant of sexually transmitted disease transmission. However, attempts to estimate sexual mixing patterns in the field remain limited partly because of practical and methodological difficulties. GOAL: To evaluate and identify appropriate sampling schemes to estimate the mixing pattern between sexual activity classes from large population networks with one or more components. STUDY DESIGN: The study is based on simulations of large population networks with various structural characteristics. A variety of snowball sampling schemes are applied to these networks and are evaluated by the quality of the mixing matrix estimates that they produce. RESULTS AND CONCLUSIONS: Unbiased estimation of mixing patterns (global assortativity, within-group mixing of the lowest activity classes, within-group mixing of the highest activity classes) from large population networks is possible with a snowball sampling design in which the initial sample of index cases is drawn from the general population, all partners of the index case are recruited, and only one generation of partners are traced (one cycle). Simulation techniques proved useful in addressing complex methodological issues in situations where analytic results are difficult to obtain.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".