Commentary: How to do personal network surveys: from name generators to statistical modeling
Bibliographic record
Abstract
Abstract The book “Conducting Personal Network Research” is a conceptual and methodological introduction to the structural study of personal networks. It is part of a series of recent monographs that have begun to systematize the knowledge generated in this area in recent decades (Crossley et al., 2015; McCarty et al., 2019; Perry et al., 2018). In this case, the authors have dedicated a large part of their career to the empirical investigation of the interpersonal relationships, interaction contexts, and social integration processes of immigrants, along with other groups in vulnerable situations. With this publication, all this experience is now reflected in a clear and comprehensive introductory text. This book explains how to integrate relational data collection and analysis with survey research. It systematically presents the strategies to estimate the size of personal networks. Finally, it describes how to fit statistical analysis to relational data, including regression models, multi-level models, and longitudinal models.
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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 | 0.038 | 0.278 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.034 | 0.063 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".