Green Spaces with Fewer People Improve Self-Reported Affective Experience and Mood
Bibliographic record
Abstract
Calm and quiet green spaces provide health benefits for urban residents. Yet as cities become more densely populated, increasing public users to green spaces may reduce or moderate these benefits. We examine how increased pedestrian density in a green street changes self-reported wellbeing. We use a between subject experimental design that added public users as confederates in randomly selected periods over three weeks. We collect data on mood and affective response from pedestrians moving through the green street (n = 504), with and without our public user treatment in randomly selected periods. Mood and affective response are improved when experiencing the green street with fewer people. We find that an increased number of public users in the green space has a negative effect on mood, especially among women. We provide experimental evidence that self-reported wellbeing in urban green spaces depends on social context, and that there are gender inequities associated with changes in affective response. Although we only measure immediate impacts, our results imply that the health benefits of green spaces may be limited by the total number of users. This research contributes additional evidence that greener cities are also healthier cities, but that the benefits may not be equally shared between women and men and will depend on the social context of use.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".