Shared.Futures: fostering convergence and envisioning possible futures through ArtScience
Bibliographic record
Abstract
Amid uncertain and complex environmental and climate futures, both science and society need an agent for active hope and shared perspectives to address these existential challenges. ArtScience, created through transdisciplinary collaboration between artists and scientists in which artistic inquiry can impact scientific inquiry, and vice versa, is one means to this end. We describe the Shared.Futures Workshop and Exhibit, based in Albuquerque, New Mexico, USA, as an example of ArtScience as convergence research and community engagement with current scientific findings. The inaugural Shared.Futures program was a five-month workshop (April-August 2022) that brought together five professional artists and five academic scientists to collaborate across disciplines and sector lines. Five workshop organizers established the goals and timeline as well as facilitated meetings to support the cohort of ArtScience teams. The workshop culminated in a month-long exhibition of the resulting artwork from the five artist-scientist pairs along with one additional project led by the workshop organizers at the Explora Science Center and Children’s Museum, Albuquerque. Inspired by principles of transition design, the Shared.Futures program nurtured a locally rooted yet globally informed dialogue, empowering collaborations between artists and scientists to explore complex wicked problems. This approach leveraged the synthesis of art and science to promote equity and co-create shared realities, exemplifying the potential of convergence research.
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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 | 0.026 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.028 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.004 | 0.038 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 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".