The Virtual Water Gallery: Measuring attitude changes towards climate and water through art
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. Yet, it is challenging to communicate science on complex and highly volatile topics such as water and climate change. Conceptualizing water-related environmental and social issues in novel ways, for example using art, with engagement between diverse audiences may lead to comprehensive solutions to these complex challenges.The Virtual Water Gallery (VWG) project, launched as part of the Global Water Futures (GWF) program in 2020 as a collaborative space merging science and art to address water challenges. Thirteen artists, representing diverse voices, teamed up with GWF scientists to explore specific challenges across Canada. The resulting artworks were exhibited on the VWG website (www.virtualwatergallery.ca) in 2021, with a first in-person exhibition in Canmore in 2022. Surveys were concurrently conducted to capture perspectives on climate change and water challenges, as well as on the role of art as a tool for engagement, from project participants, online and in-person gallery visitors.Join us as we share key findings and lessons learned on the SciArt collaborations and exhibition. Participant survey results highlight the participating artists and scientists’ experiences during the co-creation process. Visitor survey results help clarify the impact of art on people's understanding of climate change and its effects on water resources, alongside insights into behaviour changes (e.g., energy conservation, recycling, dietary choices) as a result of visiting the exhibition.
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.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".