WHEN CLOSE TIES LIVE FAR AWAY: PATTERNS AND PREDICTORS OF GEOGRAPHIC NETWORK RANGE AMONG OLDER EUROPEANS
Bibliographic record
Abstract
Abstract Using the Survey of Health, Ageing and Retirement in Europe (SHARE, Wave 6 in 2015), this paper examines the structure of older adults’ core discussion networks in terms of their geographical outreach. We also examine how far respondents live from their friends, and how such a connection is conditioned by the presence of a proximate child in the network. Findings suggest that older adults in Northern Europe are more likely to have a confidant at mid- and long-range (5-25km and >25km, respectively) than seniors in Central Europe, while their counterparts from Eastern and Southern Europe are less likely to identify a discussant out of their 5km radius. This pattern persists when focusing only on non-kin members of one’s network. However, having a nearby child confidant does not affect the probability of being connected to friends at variant distances in North Europe, while it does predict a lower likelihood of having close-by (0-5km) and long-distance (>25km) friends in Eastern and Southern regions. Other significant predictors of one’s geographical network reach, such as education, financial standing, cognitive ability, computer skills, and car ownership are also discussed and compared across European regions.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".