Emergent properties and structural patterns in sexually transmitted infection and HIV research
Bibliographic record
Abstract
BACKGROUND: Despite remarkable progress in the scientific understanding of the biological characteristics of the pathogens, pathogenesis and immunology, human sexual behaviour and population transmission dynamics, there are still considerable knowledge gaps regarding the heterogeneity and determinants of epidemics of sexually transmitted infections (STI) and HIV. To understand more fully the causes of STI and HIV epidemics it is necessary to reconcile individual and population approaches and bring together sociological and biomedical streams of research. METHODS: This study examined the implications of approaching the study of STI and HIV epidemics from the perspective that individual and population-level characteristics and interactions result in emergent properties and structural patterns that cannot be easily predicted. In addition to offering examples from the research literature, female sex work is analysed as an example of a complex adaptive system and the implications for STI and HIV epidemics are examined in that context. RESULTS: Previous research in this field has provided compelling examples of how the complex interplay of individuals and resulting structural patterns including sexual networks can influence the patterns of emergence and the trajectory of STI and HIV epidemics. CONCLUSIONS: Approaching the study of STI and HIV epidemics as emergent phenomena arising from complex interactive systems with diverse structural patterns offers a promising avenue for developing a more coherent understanding of these epidemics. It would also promote consilience between sociological, population and biomedical disciplines that could open new vistas for the science of public health.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".