Developing a Robust Design Criteria Using Clothing Taskscape and FEA Model
Bibliographic record
Abstract
A robust 60-point design criteria that guided the creation of a three-part functional outdoor winter clothing system for seated clients was developed using the clothing taskscape (Tullio-Pow & Strickfaden, 2020) and the FEA model (Lamb & Kallal, 1992). This paper illustrates how to transform a detailed clothing taskscape into a design criteria that can guide the design process through a three phases of data analyses including: categorizing primary research into themes; categorizing basded on the generic clothing taskscape; looking at bodily relationships for sporting activities. The design criteria was created in five parts: (1) required visual styling; (2) required safety attributes; (3) fabric performance requirements; (4) use and placement of fasteners; and (5) fit with the body. The research herein provides essential materials that show designers and educators how to create a design criteria by using the clothing taskscape and FEA model.
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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 | 0.000 | 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) | 0.000 | 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; a candidate call from one teacher head, 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".