Developing a mapping from affective words to design parameters for affective design of apparel 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
The development of an emotion-based (or affect-based) apparel design system has become an important issue nowadays due to the customer’s increased demand for apparel products not only in the aspect of function but also of aesthetics or affect/emotion. This paper presents a study on developing a mapping from affective words to design parameters. The technique employed to develop this mapping is neural networks (NNs). Both linear NNs and higher-order NNs were applied. An example was taken to illustrate and validate the developed mapping. There are two main contributions from the study. The first is that this mapping is the first in the domain of apparel design, and with it, the computer-aided affect-based design for apparel becomes possible. The second one is the provision of some empirical knowledge for the evaluation of so-called higher-order NNs.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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