Using Human-Centered Methods to Inform Designing
Bibliographic record
Abstract
This workshop introduced qualitative methods with a human centered lens. Communication with people in natural settings prior to developing products reveal the interconnections between people's thoughts and actions and are essential to identify design criteria and new ways of designing. This session outlined a variety of human centered methods that combine observation with the use of probes to encourage storytelling to facilitate the designer becoming familiar with the use scenario, formulating curiosity, questions, and insight. Topics included building empathy through body mapping and using observation to gain design insights, co-designing with personalized body scans, mapping the clothing taskscape as well as immersive interview protocols that employed the use of probes, guided tours, talking whilst shopping, and personal wardrobes. Methodological benefits and limitations were described and illustrated using tangible examples rich with details of data collected from diverse groups within a variety of clothing contexts.
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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 | 0.136 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".