Analysis of Spatial Area Vegetation Design Factors on Vandalism Intentions of Visitors to Tropical City Park (Surabaya-Indonesia)
Bibliographic record
Abstract
This study provides an overview of how the spatial area design of Tropical City Park (TCP) can influence the discomfort of visitors to the point of having an impact on bad behavioral intentions or vandalism.This study pays attention to how the design of the area vegetation spatial and spatial area governance at TCP Surabaya-Indonesia can influence visitors' vandalism intentions, which is an interesting topic of discussion that has not been widely discussed by previous researchers.The micro-macro control analysis method of the park spatial area was carried out with two stages of quantitative descriptive and linear regression was carried out on 515 adult TCP visitors aged >17 years who had been active in the comprehensive Tropical City Park spatial area of Surabaya, Indonesia.The results of the study showed that there was a correlation with the stimulus of individual visitor vandalism intentions with vegetation management factors, temperature, and activity control in the TCP spatial area, but there were differences in the results of group vandalism intentions where the territoriality and access control factors were less significant.This study can provide a more comprehensive picture of the spatial area governance factors of TCP affecting discomfort and disruption to visitor activities which can further stimulate various negative behavioral intentions to vandalism.In-depth studies related to the spatial area of the park can be a concern for park managers in developing better services and policies for their consumers and are also expected to be able to reduce the impact of vandalism.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".