Residents’ Preferences and Perceptions toward Green Open Spaces in an Urban Area
Bibliographic record
Abstract
Green open space is an important part of the natural–social ecosystem, providing ecological services that maintain the healthy development of cities and society. Residents’ perceptions of these benefits are largely related to their social-economic background as well as their familiarity with the development of green open spaces in their neighborhoods. Understanding residents’ perceptions of green open space will contribute significantly to urban planning by providing practical information that facilitates residents’ needs. Using the urban development zone (UDZ) of Wuhan, this study aims to understand residents’ preference toward green open space and their perceptions of ecological services and improvement, with the focus on the linking between social factors, preference, and views. In this study, data are collected through online questionnaire surveys and interviews. The results demonstrate how respondents’ views vary and which social factors significantly relate to them. Significant changes in natural space changes are reflected in the public’s perception of the ecological functions of these spaces. Responses to improving green open space reflect the residents’ pursuit of natural affinity and practicality. We conclude that it is better to enhance public involvement by providing residents’ views, which helps to recognize actual needs in long-term green open space planning.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".