Bodies of Empathy: A Data-Driven Approach to Fashion Design
Bibliographic record
Abstract
Bodies of Empathy aims to explore the role of fashion design and wearable technology in data physicalization for enhancing empathy. By mapping data into the physical form of garments, this research attempts to bring data back to the physical world and make it relatable to the people from whom it has been collected. Data can be too complex for non-experts to understand. It is necessary for information designers to find new and engaging ways to communicate the insights from this data to everyday people so they can be empowered to understand and act on it for the betterment of society. The field of fashion design, which considers garments/clothing as an extension of the body, can give insight into how designers can leverage on the affordances of garments in the communication of complex information. Wearable technology garments have the power to extend our embodied senses, enhancing our understanding of data and inspiring empathy. Through engaging in an alternative fashion design process, devised from combining research through design, data visualization and soma design methods, the project maps African Immigrant data onto garments. These data-based garments created are aimed at inspiring empathy in wearers to improve emotional connectedness.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".