Creating through the art of another: Exploring extreme ocean events via "Exquisite Corpse"
Bibliographic record
Abstract
\cite{a2021,Jung_2021} \cite{Jung_2022,r2014} \cite{a2022} Among the many benefits of ArtScience collaborations are the opportunities afforded to approach issues through the eyes of another. This might mean looking through a different disciplinary lens, engaging with unfamiliar individuals or communities, or deliberately seeking to open up intellectually and emotionally by diving into new perspectives. Through the universal language of art we illustrate such a process, using the "Exquisite Corpse" method to highlight different ways of interpreting extreme ocean events among an interdisciplinary group of artists and scientists. Over a six-week period, participants created series of three artworks inspired by a compilation of scientific imagery, data and news clips relating to the Hunga Tonga underwater volcano eruption in Tonga, 20 December 2021. At the end of each two week period, participants exchanged individual artworks, which served as inspirational seeds for subsequent interpretive creations, and thereby engaging participants in a process of deep reflection on one another's perspectives without need for translation between artforms. When each participant had completed three artworks, all participants met to view, discuss and celebrate the full collection. The wide variety of narrative and artistic approaches explored showcases the multiplicity of approaches for interpreting and connecting to this scientific topic. The various series of artworks that build on one another demonstrate how creating as a response to the art of another makes space for exploration of new ideas and ways of thinking in a fun and emotionally engaging way. They also demonstrate the importance of giving space to various narratives of connection, creating a plurality of stories, perspectives and insights. The "Exquisite Corpse" approach is a pathway to transdisciplinary collaboration that creates a holding space for the coexistence of multiple ways of observing, interpreting, understanding and relating that is greater than the sum of its parts.
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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 | 0.003 | 0.004 |
| 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 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".