Green versus Gray: Attitudes toward Vegetation in a Tropical Metropolitan Square
Bibliographic record
Abstract
Urban squares play an increasingly important role as spaces with green areas where residents can connect with nature and consume ecological amenities. Our research investigated user attitudes and preferences towards the presence, use and benefits of green infrastructure in Convalecencia Square, a main city square in San Juan, Puerto Rico with an unusual dual configuration and regulatory structure. Users were interviewed to assess preferences and attitudes towards vegetation and uses. A tripartite model that considered cognitive, affective and behavioral components was used to examine attitudes towards green areas, and choice experiment based methodology facilitated assessment of user preferences towards existing and hypothetical gray/green infrastructure configurations. Findings show that, with the exception of those expressing visions akin to those of the hygienic city imaginary, users show strong affective outlooks towards green infrastructure, placing emphasis on aesthetic components, and exhibiting a limited identification of cognitive components such as the role of ecosystem services. Education on green area ecosystem services could help bridge the gap between affective and cognitive components. Furthermore, gender differences on green infrastructure configuration choices point to the idea of exploring gender-differentiated green area conservation and development strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".