Simplifying the Personal Network Name Generator
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Researchers studying personal networks often collect network data using name generators and name interpreters. We argue that when studying social support, multiple name generators ensure that researchers sample from a multidimensional definition of support. However, because administering multiple name generators is time consuming and strains respondent motivation, researchers often use single name generators. We compared network measures obtained from single generators to measures obtained from a six-item multiple-name generator. Although some single generators provided passable estimates of some measures, no single generator provided reliable estimates across a broad spectrum of network measures. We then evaluated two alternative methods of reducing respondent burden: (1) the MMG, a multiple generator using the two most robust name generators and (2) the MGRI, a six-item name generator with name interpreters administered for a random subset of alters. Both the MMG and the MGRI were more reliable than single generators when measuring size, density, and mean measures of network composition or activity, though some single name generators were more reliable for measures consisting of sums or counts.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it