Leisure for whom? Socioeconomic disparities in the well-being benefits of third places
Bibliographic record
Abstract
With increasing divestments from physical community spaces and the rise of online social networks, there is renewed interest in how third places—places that are not home or work—may support a happier life. However, a key consideration often overlooked is that experiencing the benefits of third places may heavily depend on the resources people have like time and money. Whereas past work in this area has largely focused on health outcomes, life satisfaction offers a measure of one’s summative assessment of their lives overall, thus allowing us to evaluate how third places may be linked to overall quality of life. This preregistered study examined whether the association between the number of third places (e.g., parks, retail stores) and life satisfaction varies by income level in a representative U.S. sample totaling 1.6 million participants. Using points of interest data from the Gallup U.S. Daily Poll and National Neighborhood Data Archive, our analyses revealed that residing in areas with more Eating and Drinking Places, Personal Care Services, Social Services, and Retail Establishments was associated with larger gains in life satisfaction for lower-income individuals than middle- and higher-income individuals. However, lower-income individuals experienced fewer gains in life satisfaction with a larger quantity of Arts, Entertainment, and Leisure than middle- and higher-income individuals. Altogether, the mere presence of more third places may not lead to benefits for all—rather, their association to life satisfaction differs significantly for individuals in different income groups.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".