Visualizing Cancer: A Transdisciplinary Art and Biology Collaborative
Bibliographic record
Abstract
It would be safe to say that nearly every student enrolled in college knows someone who has been impacted by cancer. After all, cancer killed nearly 8.2 million people worldwide in 2012 (World Cancer Report, 2014). Using this fact as the impetus for change we decided to make cancer the focus of a “transdisciplinary” (Marshall, 2014) collaborative effort to simulate a reciprocal-learning experience between undergraduate biology and visual art students attending a university in Southeastern Michigan. The goal of the 2015 project was to create an active and authentic collaboration utilizing the university visual art and biology curricula. By engaging and connecting scientific and artistic critical thinking processes, we wanted to know: Could we design a class structure that would enable collaborative teams of art and biology students to create a visual model that represents a hallmark of cancer designed so that the model could also stand alone on artistic merit? In other words, could cancer visualization be transformed into works worthy of gallery display while maintaining scientific accuracy? In this paper we discuss the planning, implementation, results, and impact this work has had upon the way we now envision transdisciplinary collaboration.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".