Long-Standing Nonkin Relationships of Older Adults in the Netherlands and the United States
Bibliographic record
Abstract
The main research questions of this study were (1) How long have adults in the Netherlands and the United States known members of their nonkin networks? (2) What are the predictors of long-standing nonkin relationships? and (3) Which predictors are recognizable in both societies? The data came from the NESTOR-LSN survey (3,229 adults aged 55 to 89 years in the Netherlands) and from the Northern California Community Study ( n = 1,050, with 225 respondents aged 55 to 91 years in the United States). In both countries, the duration of nonkin relationships was related to the absence of network-disturbing variables (e.g., the number of years since the last move), network-sustaining variables (e.g., distance to nonkin), and other network properties (e.g., homogeneity). Nationally based differences were also observed (e.g., having a car was related to stable relationships only in the United States, and the special integrative functions of exclusive friendships were elicited only in Europe).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".