Evaluation-perception of site attributes and plant species selection in the public urban green space of a compact city
Bibliographic record
Abstract
Understanding citizens’ evaluation of public urban green space (UGS) attributes and plant species features can inform greenspace design to meet public expectations. This study evaluated the public’s responses to UGS attributes and plant species in Hong Kong using a questionnaire survey of 827 adult respondents. Principal component analysis followed by cluster analysis were applied to analyze the data. The respondents were differentiated into three groups (ecological, eclectic, and pragmatic users) based on the evaluations of UGS attributes. Additionally, three clusters (conservation supporters, all-round perfectionists, and safety defenders) were classified based on evaluating plant species features. Plant knowledge and gender were the main factors associated with respondents’ evaluation profiles. Respondents with different expectations of UGS attributes harbored different evaluations of plant species features. The respondent groups agreed unanimously that similar plant species composition was deployed across UGS sites in Hong Kong. Respondents attaching importance to the conservation value of plant species (i.e., “conservation supporters”) were more concerned about plant species selection. The conservation supporters were dissatisfied with the current plant selection strategy. A zonation strategy for large UGS could cater to a broad range of user demands and create a socially-inclusive venue for residents. Alternatively, a collection of small UGS in a given district can cover a range of functions. The findings could inform a modified approach to UGS design and plant selection to satisfy the residents’ disparate expectations and needs.
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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) | 0.002 | 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".