Linking human wellbeing and urban greenspaces: Applying the SoftGIS tool for analyzing human wellbeing interaction in Helsinki, Finland
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article reviews a study into the relationships between greenspaces and the benefits to psychological, social, and physical aspects of human wellbeing achieved through interaction in the Helsinki urban region in Finland. This relationship is theorized, analyzed, and measured through the transactional paradigm of affordance theory and is operationalized through the use of a public participation geographic information system (PPGIS) questionnaire, SoftGIS, which activated the urban greenspace–human wellbeing interaction through its map-based data collection. Over 1800 unique place–based relationships were statistically analyzed. Findings revealed that Helsinki’s greenspaces provided, overall, mostly physical and social wellbeing benefits; the psychological benefits such as reduction in stress and mental relaxation were not as frequent in these urban greenspace interactions. The results indicate multiple aspects of human wellbeing are supported by interaction with urban greenspaces of varying characteristics within the region but the urban greenspaces which provided the most human wellbeing benefits included large size, woodland typology, moderately maintained with loose or ‘wild’ vegetation, and few amenities such as benches and structures. The study’s implications include urban planning, public policy, and human health as well as insight into the multifunctional design and strategic management of greenspaces in urbanizing regions to provide continued and improved ecosystem services and benefits to humans and nature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it