Bibliographic record
Abstract
Science and art are often disconnected but, if combined, can help stimulate learning and novelty and guide societal change. How then to bridge the divide between scientists and artists in a way that extends beyond superficial, short-term interactions? We describe an ongoing coproduction practice between a Swedish sustainability scientist and two Chilean artists—a sculptor and painter—striving to find ways to work together. Our transdisciplinary collaboration was initiated in 2013 and, although there has never been an agenda or goal for our interaction, there has been a mutual interest to investigate joint possibilities. Through a series of meetings, we tried but failed to accomplish anything for several years. By 2022, we finally created something tangible together, realizing it was not just material objects we were producing but also a meeting between worlds. We describe how this long-term partnership, driven by mutual respect and curiosity, created conditions for bridging across our respective knowledge and practices. By working, walking, and exploring together, we learned how to communicate, overcome challenges of different languages, and combine perspectives. We have recognized similarities in how we engage with material from the natural world and how we combine elements for novelty. Through our interactions, we have started to identify how coproduced science and art can stimulate a reconnection with the biosphere, thereby providing a foundation for transformative societal change.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.015 | 0.036 |
| Scholarly communication | 0.019 | 0.033 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.060 | 0.028 |
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".