Inferring Structural Characteristics of Networks with Strong and Weak\n Ties from Fixed-Choice Surveys
Bibliographic record
Abstract
Knowing the structure of an offline social network facilitates a variety of\nanalyses, including studying the rate at which infectious diseases may spread\nand identifying a subset of actors to immunize in order to reduce, as much as\npossible, the rate of spread. Offline social network topologies are typically\nestimated by surveying actors and asking them to list their neighbours. While\nidentifying close friends and family (i.e., strong ties) can typically be done\nreliably, listing all of one's acquaintances (i.e., weak ties) is subject to\nerror due to respondent fatigue. This issue is commonly circumvented through\nthe use of so-called "fixed choice" surveys where respondents are asked to name\na fixed, small number of their weak ties (e.g., two or ten). Of course, the\nresulting crude observed network will omit many ties, and using this crude\nnetwork to infer properties of the network, such as its degree distribution or\nclustering coefficient, will lead to biased estimates. This paper develops\nestimators, based on the method of moments, for a number of network\ncharacteristics including those related to the first and second moments of the\ndegree distribution as well as the network size, using fixed-choice survey\ndata. Experiments with simulated data illustrate that the proposed estimators\nperform well across a variety of network topologies and measurement scenarios,\nand the resulting estimates are significantly more accurate than those obtained\ndirectly using the crude observed network, which are commonly used in the\nliterature. We also describe a variation of the Jackknife procedure that can be\nused to obtain an estimate of the estimator variance.\n
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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 | 0.008 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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 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".