Investigation of the Emotional Characteristics of White for Designing White Based Products
Bibliographic record
Abstract
In this study we investigated emotional characteristics of various shades of whites which have slightly different nuances to suggest guidelines that will help designers to select the appropriate colors when designing white based product. The study involved three different procedures. In Experiment 1, we selected 20 emotional words through a survey (N=30) among 60 words, which we picked from literature review and workshop and was thought to be appropriate to evaluate product colors. In Experiment 2, we evaluted the emotions of 13 basic colors from the I.R.I Hue&Tone 120 system (N=30) using the 20 previously selected emotional words, to find relative emotional positions of white in comparison to other colors. Finally, in Experiment 3, we conducted an emotional evaluation on various shades of whites using the extracted factors. The color stimuli used in each of the three experiments were measured in terms of CIE 1976 L*a*b color space. Throughout the three empirical studies, we observed three overruling tendencies : First, there are four important factors when evaluating product color – flamboyant, elegant, clear and soft; second, white is dominantly the most elegant in comparison to other colors; third, every emotional factor of the study was affected by hue, saturation and brightness.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".