Investigation of the Emotional Characteristics of White for Designing White Based Products
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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 it