Design Guidelines for the Hot Spring Renovation by Participatory Action Research (PAR) and Design Thinking for Sustainable Health Tourism Promotion
Bibliographic record
Abstract
The aim of this article is to provide principles for improving hot spring spaces and promoting them as sustainable health tourism destinations through participatory design and design thinking, using Wang Hin Hot Spring Park in Bang Khan District, Nakhon Si Thammarat Province, Thailand as a case study, hearing activities were held with all stakeholders to collect data for analysis, the architectural design process was integrated. While the area has potential, many relevant sectors have yet to develop it or promote it coherently. The study results show that the improvement guidelines are as follows: Define a public/private zone. Control the entrance and exit to have only one point. Improve the most used ponds to accommodate everyone's use. Modify an existing space that is not suitable for the project to meet the project's needs. Create connected routes. Empty spaces should be utilized as efficiently as possible. Improve the parking areas to facilitate tourism-related travel. Increase the space to encourage wellness and align with the community's potential.
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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 | 0.154 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".