Visualizing Cancer: A Transdisciplinary Art and Biology Collaborative
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it