Social isolation by design: Bias in measuring core networks in Taiwan?
Bibliographic record
Abstract
The estimation and measurement of the size of egocentric networks have sparked vigorous discussion and debate. Drawing on datasets from the Taiwan Social Change Survey, this study explores methodological issues pertaining to the change of core networks in Taiwan from 1997 to 2017 via a modified Poisson mixture approach, assesses the efficiency of name generators as a survey instrument via Fisher Information Maximizer, and investigates the role of social desirability in reporting core networks. Net of other effects, the study finds that individuals expressing a strong sense of social desirability report significantly fewer close contacts and face a higher risk of social isolation. Name generators in this study are associated with trivial design errors and can yield estimates comparable to those produced by exact enumeration. These findings are situated in the drastic changes in face-to-face survey interviews as well as the cultural context of Taiwan and, more broadly, East Asia. They call for further research inquiries into methodological issues regarding measuring and estimating egocentric networks in a transnational and modern setting.
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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 | 0.122 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| 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".