Ninety years after Lewin: The role of familism and attachment style in social networks characteristics across 21 nations/areas
Bibliographic record
Abstract
Drawing on the literature on person-culture fit, we investigated how culture (assessed as national-level familism), personality (tapped by attachment styles) and their interactions predicted social network characteristics in 21 nations/areas ( N = 2977). Multilevel mixed modeling showed that familism predicted smaller network size but greater density, tie strength, and multiplexity. Attachment avoidance predicted smaller network size, and lower density, tie strength, and multiplexity. Attachment anxiety was related to lower density and tie strength. Familism enhanced avoidance’s association with network size and reduced its association with density, tie strength, and multiplexity. Familism also enhanced anxiety’s association with network size, tie strength, and multiplexity. These findings contribute to theory building on attachment and culture, highlight the significance of culture by personality interaction for the understanding of social networks, and call attention to the importance of sampling multiple countries.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".