Are Workers Attracted to Social Interaction Opportunities? A Study of Face-to-Face Contact Opportunities by Occupation and Industry
Bibliographic record
Abstract
The spatial structure of a region is known to affect the degree of face-to-face interaction opportunities for a city’s residents. These interaction opportunities are important building blocks in aspects of economic production. To date, though, there is scant empirical evidence linking interaction opportunities to worker locations. In this article, using a spatial measure of social interaction potential (SIP), we seek to discover whether, and by how much, opportunities for interaction differ at home and work locations for workers within different industry and occupation groups in U.S. metropolitan areas. Based on the time-geographic concept of joint accessibility, SIP is sensitive to population and employment densities, as well as travel times associated with worker commutes in a region. We compare SIP at the census tract level of geography both within and between Metropolitan Statistical Areas (MSAs) nationally and test SIP distributions by occupation and industrial categories using nonparametric Kruskal–Wallis and Dunn tests. The study finds that several categories of higher skill and creative workers live and work in higher SIP areas. These findings provide evidence in support of theories of knowledge creation that rely on spontaneous face-to-face interaction and also indicate the effect of lifestyle preferences in location choices for highly skilled and arts workers.
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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 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".