On the Locality of Asymmetric Close Relations: Spatial Proximity and Health Differences in a Senior Community
Bibliographic record
Abstract
OBJECTIVE: A long line of research maintains that physical proximity increases interpersonal contact and boosts the likelihood of voluntary relationships. Proximity effects, however, may be modified by additional, valued characteristics that distinguish people from one another, such as physical health. I examine this interaction between proximity and assortative mechanisms with a complete network of retirement community (RC) residents. METHODS: Descriptive statistics and an exponential random graph model (ERGM) are used to analyze ties between 123 RC residents. In addition to hypothesized variables, the ERGM approach accounts for structural network processes that generate ties. RESULTS: As expected, reports of close relationships were strongly influenced by physical proximity. Also consistent with hypotheses, close tie nominations tended to be asymmetric along a health gradient: People were less likely to identify those in worse health than themselves as a close tie. Physical proximity, moreover, intensified the health-based asymmetries. DISCUSSION: Findings suggest that relational inequalities associated with health are most pronounced when they are most local. I conclude by noting broader implications for the study of social networks, health, and physical space among older adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".