How Far and with Whom Do People Socialize?
Bibliographic record
Abstract
Hägerstrand's seminal argument that regional science is about people and not just locations is still a compelling and challenging idea when the spatial distribution of activities is studied. In the context of social activity–travel behavior (hosting and visiting), this issue is particularly fundamental as individuals’ main motivation in making social trips is mostly with whom they interact rather than where they go. A useful approach to incorporate the travelers’ social context is to study explicitly the spatial distribution of their social networks, focusing on social locations as emerging from their contacts, rather than analyzing social activity locations in isolation. In this context, this paper studies the spatial distribution of social activities, focusing on the home distances between specific individuals (egos) and the network members (alters) with whom they socialize—serving as a proxy to study social activity–travel location. Using data from a recent study of personal networks and social interaction, and multilevel models that account for the hierarchical structure of these networks, this paper provides empirical evidence on how the characteristics of individuals and their social context relate to the distance separating them. The results strongly suggest that, although the spatial distribution of social interaction has idiosyncratic characteristics, there are several systematic effects associated with the characteristics of egos, alters, and their personal networks that affect the spatial distribution of relationships, and they can contribute to an understanding of where people perform social activities with others.
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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 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".