Citizen Sensing within Urban Greenspaces: Exploring Human Wellbeing Interactions in Deprived Communities of Glasgow
Bibliographic record
Abstract
The relationship between urban greenspaces and the benefits to psychological, social, and physical aspects of human wellbeing are important to study, particularly in rapidly urbanizing areas and underrepresented communities. This interaction was theorized, analyzed, and measured in this paper through the transactional paradigm and operationalized through the use of a volunteer geographic information questionnaire, SoftGIS, which activated the urban greenspace–human wellbeing interaction through its map-based data collection. Over 450 unique place-based relationships were statistically analyzed within the Greater Glasgow Urban Region of Paisley, Scotland, a vulnerable community. This study revealed that multiple components of human wellbeing are supported through interactions with urban greenspaces. The Paisley region’s respondents visited greenspaces, generally, and most often to receive psychological benefits such as reduction of stress and mental relaxation through interactions which included sitting and relaxing in quiet spaces, enjoying natural surroundings, and viewing nature and wildlife. The physical and social wellbeing benefits were not as frequent in these urban greenspace interactions but were distinctly present. The results imply pathways towards management and multifunctional greenspace design responses in urbanizing regions and indicate strategies for public policy, human health, and urban planning, which deliver wellbeing benefits to communities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".