Plastic pollution as a canvas for change: fostering collaboration for environmental solutions and actions through art and science
Bibliographic record
Abstract
Meaningful action and engagement are needed in a time of rapid planetary change and biodiversity loss. They cannot be achieved through scientific outputs alone, and scientists are increasingly recognizing the need to work with a diverse range of collaborators to communicate their research and engage society. We used a semi-structured survey of 34 previous artistic collaborators with our research group, the Adrift Lab, to collect information on their motivations, rewards, challenges, and lessons learned from a wide array of projects ranging from furniture and jewelry design to documentary filmmaking. Clear patterns emerged, including that participating in an art-science collaboration with Adrift Lab resulted in a greater sense of community, an ability and empowerment to make meaningful contributions to environmental issues, and inspiration for artists to shift the focus of their work, leading to additional, environmental-focused collaborations with other scientists. How artists discovered Adrift Lab’s research and the reasons they chose to engage with our research was somewhat unexpected, with more traditional modes of outreach such as conference presentations and the Adrift Lab website having little influence. Instead, artists often selected Adrift Lab as a collaborator based on their perception that our group was approachable and readily shared ideas and knowledge. These results highlight the willingness of many artists to collaborate with scientists, the mutual benefits of these relationships, and advice for others looking for unique ways, small or large, to engage with new audiences. We conclude with our own recommendations for scientists who wish to collaborate with artists and our enthusiastic advice to do so.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.023 | 0.020 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.004 | 0.037 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".