The Value of Arts-Based Methods in STEM: Formal Analysis, Open Dialogue, and Subjectivity
Bibliographic record
Abstract
In the Fall of 2021, STEM researchers were invited to participate in a series of SSHRC-funded workshops delivered at the University of Guelph’s School of Fine Art and Music (SOFAM), where they examined a work of abstract art drawn from the SOFAM Print Study Collection (Reflex Victory by Chrysanne Stathacos, lithograph, 1979). The project’s objective was to determine if methodologies used in the analysis and interpretation of art are helpful to researchers who use visual observation as a primary method of collecting data. Our findings indicate that over the duration of the one-hour workshop, participants demonstrated greater confidence in identifying what lay in their fields of vision with precision, exhibited greater comfort in pursuing open-ended inquiry, and became more conscious of the mutable and subjective qualities of their looking. This report shares the story of our experiment and presents our preliminary findings on the value of arts-based methodologies in developing skills in data collection and analysis. This research contributes to the discourse on the role visual art can play in practices of teaching, learning, and research that extend beyond the studio, museum, and gallery space.
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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 | 0.180 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.011 | 0.071 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".