The Virtual Water Gallery: Art as a catalyst for transforming knowledge and behaviour in water and climate
Bibliographic record
Abstract
Water is life. Water-related challenges, such as droughts, floods, water quality degradation, permafrost thaw and glacier melt, exacerbated by climate change, affect everyone. It is challenging, yet of critical importance, to communicate science on such difficult highly volatile topics. Art is a more approachable medium to traditional scientific outlets that has the potential to diversify voices at the table and to lead to more wholistic solutions to these complex challenges. Launched in 2020, the Virtual Water Gallery is a transdisciplinary science and art project of the Global Water Futures program, that aims to provide a collaborative space for dialogues between water experts, artists, and the wider public, to explore water challenges we all face. As part of this initiative, 14 artists or sci-artists representing women, men and Indigenous voices across Canada were paired with teams of Global Water Futures scientists to co-explore specific water challenges in various Canadian ecoregions and communities. These collaborations led to the co-creation of artworks exhibited online on the Virtual Water Gallery in 2021. In 2022, the Virtual Water Gallery came to life with an in-person exhibition in Canmore, Alberta, Canada. Surveys were developed to capture changes in knowledge, attitudes and water-related climate mitigation practices of visitors to this science and art online and in-person exhibition. Surveys were also developed to capture experiences of the SciArt collaboration participants. Results from the survey responses of 139 visitors hint to the significance of art in changing knowledge levels and intended behaviours related to water-related climate change mitigation, especially for visitors with low prior knowledge levels. This underscores the potential of SciArt to extend beyond communication, acting as a catalyst in the collaborative creation of new knowledge for the benefit of society. The insights gained from participant responses can serve as valuable guidance for shaping future initiatives.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 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".