The role of social media and AI in increasing willingness to pay and its implications on the quality of urban park design
Bibliographic record
Abstract
Social media and artificial intelligence (AI) are increasingly playing an important role in the process of designing city parks by influencing people's willingness to pay. This research aims to explore how social media and AI can increase willingness to pay and its impact on the quality of urban park design in Palangkaraya City, Central Kalimantan. Method: This research uses a mixed approach with a combination of quantitative surveys and qualitative interviews. The survey was conducted on 200 respondents from Palangkaraya residents to measure the influ-ence of social media and AI on their willingness to pay. Social media data analysis was con-ducted using AI tools to identify popular garden design preferences. Data was collected from social media platforms as well as in-depth interviews with stakeholders. The research results show that there is a significant positive correlation between social media involvement and peo-ple's willingness to pay. AI data from social media analysis reveals the most popular garden de-sign elements, which then influences design decisions. The quality of park designs that inte-grate feedback from social media and AI recommendations receive higher ratings from the pub-lic. These findings indicate that social media and AI can significantly increase willingness to pay and the quality of urban park design. Active engagement of the public through social me-dia helps formulate designs that better suit their needs, while AI ensures designs that are more innovative and responsive to public preferences. This research emphasizes the importance of using digital tools to improve community engagement and urban planning outcomes.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".