Evaluating the Impact of a Comprehensive Canadian Science-Art Residency Program on the Participating Scientist, Artist and the Public
Bibliographic record
Abstract
Science-art residencies can provide opportunities for insightful cross-disciplinary collaborations, science communication, and engagement with the general public. Currently, there are few formal ways for artists and scientists to collaborate in Canada, and even fewer publications on how these experiences can impact learning in informal settings. Art the Science (a Canadian non-profit organization facilitating cross-disciplinary relationships between artists and scientists) piloted a comprehensive multiphase science-art residency program. Phase 1 informed the artist’s work through a full-time experience in a scientific laboratory at an academic institution, Phase 2 showcased the artist’s final artwork, Between the Sand at an off-campus local community event, and Phase 3 published an interactive online version of the work for global exhibition. Residency evaluation in each phase was conducted through the use of qualitative and quantitative methods, including interviews, concept mapping, video diaries, and surveys. The artist, scientist and lab members gained new perspectives and inspiration about their respective fields. The artist was able to incorporate theories and processes from the research group into their artistic practice. On the other hand, the scientist saw renewed enthusiasm and curiosity within their research lab, and the lab members reported new ways of thinking about how to communicate their research. Both exhibitions proved to be engaging informal learning experiences for 66.2% of survey participants, and revealed several major learning themes. Despite promoting both events as artwork exhibitions, 79.2% of survey participants considered Between the Sand as both an artwork and a science communication product suggesting that science-based art may have the potential to communicate science, even when it is not presented as a science communication effort. Public responses revealed that public perception of funding is not skewed to either discipline and instead seems to call upon both science and arts grants to fund such interdisciplinary initiatives. Providing comprehensive artist residencies in science labs may have a valuable impact on everyone involved: the artist, the research group, and the public.
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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 | 0.020 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".