Plastic pollution as a canvas for change: fostering collaboration for environmental solutions and actions through art and science
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it