Sociometric network analysis in illicit drugs research: A scoping review
Bibliographic record
Abstract
BACKGROUND: Sociometric or whole network analysis, a method used to analyze relational patterns among social actors, emphasizes the role of social structure in shaping behaviour. Such method has been applied to many aspects of illicit drug research, including in the areas of public health, epidemiology, and criminology. Previous reviews about social networks and drugs have lacked a focus on the use of sociometric network analysis for illicit drugs research across disciplines. The current scoping review aimed to provide an overview of the sociometric network analysis methods used in illicit drugs research and to assess how such methods could be used for future research. METHODS: A systematic search of six databases (Web of Science, ProQuest Sociology Collection, Political Science Complete, PubMed, Criminal Justice Abstracts, and PsycINFO) returned 72 relevant studies that met the inclusion criteria. To be included, studies had to mention illicit drugs and use whole social network analysis as one of their methods. Studies were summarized quantitatively and qualitatively using a data-charting form and a description of the studies' main topics. RESULTS: Sociometric network analysis in illicit drugs research has grown in popularity in the last decade, using mostly descriptive network metrics, such as degree centrality (72.2%) and density (44.4%). Studies were found to belong to three study domains. The first, drug crimes investigated network resilience and collaboration patterns in drug trafficking networks. The second domain, public health, focused on the social networks and social support of people who use drugs. Finally, the third domain focused on the collaboration networks of policy, law enforcement, and service providers. CONCLUSION: Future illicit drugs research using whole network SNA should include more diverse data sources and samples, incorporate mixed and qualitative methods, and apply social network analysis to study drug policy.
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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 | 0.023 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.041 | 0.033 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".