Bibliographic record
Abstract
When fashion designers, retailers, and families attend Vancouver Kids Fashion Week, they expect to see models donning the latest in children’s fashion. But at an event last October, attendees saw scientists as well. Researchers from Michael Kobor’s lab at the University of British Columbia in Vancouver, Canada, had set up a booth to showcase some of the model organisms they study. The fashion twist: Those model organisms later appeared on the runway, on a line of outfits created by fashion designers. Mechanical engineer Angela Chang and designer Harry Umen came together to create the “Luminous Firefly Dress,” aiming to evoke ideas about how body gesture, light, color, and sound enhance personal expression. Image credit: Howard Eglowstein (photographer). The collaboration between the Kobor lab and the fashion school at Vancouver Community College in British Columbia, Canada, started several months earlier. The researchers were desperate for ways to share their work with a new audience. Some lab members work on human genetics and epidemiology; others on basic research using animal models. The intricacies of the latter research, they found, weren’t easy to share with the general public. “The model organism research is just as important, but not as visible,” says graduate student Samantha Schaffner, who studies the epigenetics of Parkinson’s disease. Schaffner and her colleagues started brainstorming ways to introduce these model organisms to a wider audience and quickly landed on the idea of a collaboration with a group outside of science. Initially, Schaffner and her team considered a wide range of art collaborators, from illustrators to jewelry designers. But Schaffner remembered another creative field from her childhood: Her mother managed a children’s clothing line, working with fashion designers to create hats called “Sam’s Tams.” Imagining the possibilities of colorful and eye-catching model organism-themed fashion, Schaffner suggested the lab contact the fashion …
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.048 | 0.020 |
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".