A Furniture Design Guideline Derived from the Socio-Economic Factors of Consumers for Their Choices of Sustainable Furniture Design in Thailand
Bibliographic record
Abstract
This article presents an environmentally friendly furniture design guideline which comes from different socio-economic conditions, including personality and physical residence of consumers in Thailand, and is a test for the perception of environmentally friendly furniture style, in order to find the style of environmentally friendly furniture in consumers with different economic backgrounds and perception of the environment. The research found that the needs for the style of environmentally friendly furniture according to different socio-economic conditions were in the same direction, which means that consumers need modern environmentally friendly furniture in western style, using natural materials in manufacturing with high technology, and furniture color in a cool shade. Therefore, the designer should consider the different socio-economic conditions of consumers that affect the needs differently, and use this as a furniture design guideline that is environmentally friendly and appropriate for the needs of consumers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.003 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".