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Record W2769467175 · doi:10.5539/ass.v13n12p182

A Furniture Design Guideline Derived from the Socio-Economic Factors of Consumers for Their Choices of Sustainable Furniture Design in Thailand

2017· article· en· W2769467175 on OpenAlexvenueno aff
Karuna Kwangsawat, Yanin Rugwongwan

Bibliographic record

VenueAsian Social Science · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsnot available
FundersKing Mongkut's Institute of Technology Ladkrabang
KeywordsEnvironmentally friendlyBusinessResidenceOrder (exchange)MarketingArchitectural engineeringEngineeringEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.050
GPT teacher head0.336
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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